{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from lifelines import CoxPHFitter\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing the proportional hazard assumptions\n",
    "\n",
    "This Jupyter notebook is a small tutorial on how to test and fix proportional hazard problems. An important question to first ask is: [_do I need to care about the proportional hazard assumption?_](#Do-I-need-to-care-about-the-proportional-hazard-assumption?) - often the answer is no. \n",
    "\n",
    "The proportional hazard assumption is that _all_ individuals have the same hazard function, but a unique scaling factor infront. So the _shape_ of the hazard function is the same for all individuals, and only a scalar multiple changes per individual. \n",
    "\n",
    "$$h_i(t) = a_i h(t)$$\n",
    "\n",
    "At the core of the assumption is that $a_i$ is not time varying, that is, $a_i(t) = a_i$. Further more, if we take the ratio of this with another subject (called the hazard ratio): \n",
    "\n",
    "$$\\frac{h_i(t)}{h_j(t)} = \\frac{a_i h(t)}{a_j h(t)} = \\frac{a_i}{a_j}$$\n",
    "\n",
    "is constant for all $t$. In this tutorial we will test this non-time varying assumption, and look at ways to handle violations. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lifelines.CoxPHFitter: fitted with 432 total observations, 318 right-censored observations>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lifelines.datasets import load_rossi\n",
    "rossi = load_rossi()\n",
    "cph = CoxPHFitter()\n",
    "\n",
    "cph.fit(rossi, 'week', 'arrest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>lifelines.CoxPHFitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration col</th>\n",
       "      <td>'week'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event col</th>\n",
       "      <td>'arrest'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baseline estimation</th>\n",
       "      <td>breslow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of observations</th>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of events observed</th>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partial log-likelihood</th>\n",
       "      <td>-658.748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2020-07-26 22:15:39 UTC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>untransformed variables</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef</th>\n",
       "      <th>exp(coef)</th>\n",
       "      <th>se(coef)</th>\n",
       "      <th>coef lower 95%</th>\n",
       "      <th>coef upper 95%</th>\n",
       "      <th>exp(coef) lower 95%</th>\n",
       "      <th>exp(coef) upper 95%</th>\n",
       "      <th>z</th>\n",
       "      <th>p</th>\n",
       "      <th>-log2(p)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>covariate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fin</th>\n",
       "      <td>-0.379</td>\n",
       "      <td>0.684</td>\n",
       "      <td>0.191</td>\n",
       "      <td>-0.755</td>\n",
       "      <td>-0.004</td>\n",
       "      <td>0.470</td>\n",
       "      <td>0.996</td>\n",
       "      <td>-1.983</td>\n",
       "      <td>0.047</td>\n",
       "      <td>4.398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>-0.057</td>\n",
       "      <td>0.944</td>\n",
       "      <td>0.022</td>\n",
       "      <td>-0.101</td>\n",
       "      <td>-0.014</td>\n",
       "      <td>0.904</td>\n",
       "      <td>0.986</td>\n",
       "      <td>-2.611</td>\n",
       "      <td>0.009</td>\n",
       "      <td>6.791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race</th>\n",
       "      <td>0.314</td>\n",
       "      <td>1.369</td>\n",
       "      <td>0.308</td>\n",
       "      <td>-0.290</td>\n",
       "      <td>0.918</td>\n",
       "      <td>0.748</td>\n",
       "      <td>2.503</td>\n",
       "      <td>1.019</td>\n",
       "      <td>0.308</td>\n",
       "      <td>1.698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wexp</th>\n",
       "      <td>-0.150</td>\n",
       "      <td>0.861</td>\n",
       "      <td>0.212</td>\n",
       "      <td>-0.566</td>\n",
       "      <td>0.266</td>\n",
       "      <td>0.568</td>\n",
       "      <td>1.305</td>\n",
       "      <td>-0.706</td>\n",
       "      <td>0.480</td>\n",
       "      <td>1.058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mar</th>\n",
       "      <td>-0.434</td>\n",
       "      <td>0.648</td>\n",
       "      <td>0.382</td>\n",
       "      <td>-1.182</td>\n",
       "      <td>0.315</td>\n",
       "      <td>0.307</td>\n",
       "      <td>1.370</td>\n",
       "      <td>-1.136</td>\n",
       "      <td>0.256</td>\n",
       "      <td>1.965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paro</th>\n",
       "      <td>-0.085</td>\n",
       "      <td>0.919</td>\n",
       "      <td>0.196</td>\n",
       "      <td>-0.469</td>\n",
       "      <td>0.299</td>\n",
       "      <td>0.626</td>\n",
       "      <td>1.348</td>\n",
       "      <td>-0.434</td>\n",
       "      <td>0.665</td>\n",
       "      <td>0.589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prio</th>\n",
       "      <td>0.091</td>\n",
       "      <td>1.096</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.148</td>\n",
       "      <td>1.036</td>\n",
       "      <td>1.159</td>\n",
       "      <td>3.194</td>\n",
       "      <td>0.001</td>\n",
       "      <td>9.476</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Concordance</th>\n",
       "      <td>0.640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partial AIC</th>\n",
       "      <td>1331.495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood ratio test</th>\n",
       "      <td>33.266 on 7 df</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-log2(p) of ll-ratio test</th>\n",
       "      <td>15.370</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "\\begin{tabular}{lrrrrrrrrrr}\n",
       "\\toprule\n",
       "{} &   coef &  exp(coef) &  se(coef) &  coef lower 95\\% &  coef upper 95\\% &  exp(coef) lower 95\\% &  exp(coef) upper 95\\% &      z &     p &  -log2(p) \\\\\n",
       "covariate &        &            &           &                 &                 &                      &                      &        &       &           \\\\\n",
       "\\midrule\n",
       "fin       & -0.379 &      0.684 &     0.191 &          -0.755 &          -0.004 &                0.470 &                0.996 & -1.983 & 0.047 &     4.398 \\\\\n",
       "age       & -0.057 &      0.944 &     0.022 &          -0.101 &          -0.014 &                0.904 &                0.986 & -2.611 & 0.009 &     6.791 \\\\\n",
       "race      &  0.314 &      1.369 &     0.308 &          -0.290 &           0.918 &                0.748 &                2.503 &  1.019 & 0.308 &     1.698 \\\\\n",
       "wexp      & -0.150 &      0.861 &     0.212 &          -0.566 &           0.266 &                0.568 &                1.305 & -0.706 & 0.480 &     1.058 \\\\\n",
       "mar       & -0.434 &      0.648 &     0.382 &          -1.182 &           0.315 &                0.307 &                1.370 & -1.136 & 0.256 &     1.965 \\\\\n",
       "paro      & -0.085 &      0.919 &     0.196 &          -0.469 &           0.299 &                0.626 &                1.348 & -0.434 & 0.665 &     0.589 \\\\\n",
       "prio      &  0.091 &      1.096 &     0.029 &           0.035 &           0.148 &                1.036 &                1.159 &  3.194 & 0.001 &     9.476 \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.CoxPHFitter: fitted with 432 total observations, 318 right-censored observations>\n",
       "             duration col = 'week'\n",
       "                event col = 'arrest'\n",
       "      baseline estimation = breslow\n",
       "   number of observations = 432\n",
       "number of events observed = 114\n",
       "   partial log-likelihood = -658.748\n",
       "         time fit was run = 2020-07-26 22:15:39 UTC\n",
       "                    model = untransformed variables\n",
       "\n",
       "---\n",
       "            coef  exp(coef)   se(coef)   coef lower 95%   coef upper 95%  exp(coef) lower 95%  exp(coef) upper 95%\n",
       "covariate                                                                                                         \n",
       "fin       -0.379      0.684      0.191           -0.755           -0.004                0.470                0.996\n",
       "age       -0.057      0.944      0.022           -0.101           -0.014                0.904                0.986\n",
       "race       0.314      1.369      0.308           -0.290            0.918                0.748                2.503\n",
       "wexp      -0.150      0.861      0.212           -0.566            0.266                0.568                1.305\n",
       "mar       -0.434      0.648      0.382           -1.182            0.315                0.307                1.370\n",
       "paro      -0.085      0.919      0.196           -0.469            0.299                0.626                1.348\n",
       "prio       0.091      1.096      0.029            0.035            0.148                1.036                1.159\n",
       "               z     p   -log2(p)\n",
       "covariate                        \n",
       "fin       -1.983 0.047      4.398\n",
       "age       -2.611 0.009      6.791\n",
       "race       1.019 0.308      1.698\n",
       "wexp      -0.706 0.480      1.058\n",
       "mar       -1.136 0.256      1.965\n",
       "paro      -0.434 0.665      0.589\n",
       "prio       3.194 0.001      9.476\n",
       "---\n",
       "Concordance = 0.640\n",
       "Partial AIC = 1331.495\n",
       "log-likelihood ratio test = 33.266 on 7 df\n",
       "-log2(p) of ll-ratio test = 15.370"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cph.print_summary(model=\"untransformed variables\", decimals=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking assumptions with `check_assumptions`\n",
    "\n",
    "New to lifelines 0.16.0 is the `CoxPHFitter.check_assumptions` method. This method will compute statistics that check the proportional hazard assumption, produce plots to check assumptions, and more. Also included is an option to display advice to the console. Here's a breakdown of each information displayed:\n",
    "\n",
    " - Presented first are the results of a statistical test to test for any time-varying coefficients. A time-varying coefficient imply a covariate's influence _relative to the baseline_ changes over time. This implies a violation of the proportional hazard assumption. For each variable, we transform _time_ four times (these are common transformations of time to perform). If _lifelines_ rejects the null (that is, _lifelines_ rejects that the coefficient is not time-varying), we report this to the user.\n",
    " - Some advice is presented on how to correct the proportional hazard violation based on some summary statistics of the variable. \n",
    " - As a compliment to the above statistical test, if the option `show_plots = True` is specified, visual plots of the the _scaled Schoenfeld residuals_ are presented for all covariates against the four time transformations. A fitted lowess is also presented, along with 10 bootstrapped lowess lines (as an approximation to the confidence interval of the original lowess line). Ideally, this lowess line is constant (flat). Deviations away from the constant line are violations of the PH assumption. \n",
    " \n",
    "####  Why the _scaled Schoenfeld residuals_?\n",
    " \n",
    "This section can be skipped on first read. Let $s_{t,j}$ denote the scaled Schoenfeld residuals of variable $j$ at time $t$, $\\hat{\\beta_j}$ denote the maximum-likelihood estimate of the $j$th variable, and $\\beta_j(t)$ a time-varying coefficient in (fictional) alternative model that allows for time-varying coefficients. Therneau and Grambsch showed that. \n",
    "\n",
    "$$E[s_{t,j}] + \\hat{\\beta_j} = \\beta_j(t)$$\n",
    "\n",
    "The proportional hazard assumption implies that $\\hat{\\beta_j} = \\beta_j(t)$, hence $E[s_{t,j}] = 0$. This is what the above proportional hazard test is testing. Visually, plotting $s_{t,j}$ over time (or some transform of time), is a good way to see violations of $E[s_{t,j}] = 0$, along with the statisical test. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The ``p_value_threshold`` is set at 0.05. Even under the null hypothesis of no violations, some\n",
      "covariates will be below the threshold by chance. This is compounded when there are many covariates.\n",
      "Similarly, when there are lots of observations, even minor deviances from the proportional hazard\n",
      "assumption will be flagged.\n",
      "\n",
      "With that in mind, it's best to use a combination of statistical tests and visual tests to determine\n",
      "the most serious violations. Produce visual plots using ``check_assumptions(..., show_plots=True)``\n",
      "and looking for non-constant lines. See link [A] below for a full example.\n",
      "\n"
     ]
    },
    {
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       "      <td>&lt;lifelines.CoxPHFitter: fitted with 432 total ...</td>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>test_statistic</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">age</th>\n",
       "      <th>km</th>\n",
       "      <td>11.03</td>\n",
       "      <td>&lt;0.005</td>\n",
       "    </tr>\n",
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       "      <th>rank</th>\n",
       "      <td>11.45</td>\n",
       "      <td>&lt;0.005</td>\n",
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       "      <th rowspan=\"2\" valign=\"top\">fin</th>\n",
       "      <th>km</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.89</td>\n",
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       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">mar</th>\n",
       "      <th>km</th>\n",
       "      <td>0.60</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.71</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">paro</th>\n",
       "      <th>km</th>\n",
       "      <td>0.12</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.13</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">prio</th>\n",
       "      <th>km</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.89</td>\n",
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       "      <th rowspan=\"2\" valign=\"top\">race</th>\n",
       "      <th>km</th>\n",
       "      <td>1.44</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>1.43</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">wexp</th>\n",
       "      <th>km</th>\n",
       "      <td>7.48</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>7.31</td>\n",
       "      <td>0.01</td>\n",
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       "  </tbody>\n",
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     "text": [
      "\n",
      "\n",
      "1. Variable 'age' failed the non-proportional test: p-value is 0.0007.\n",
      "\n",
      "   Advice 1: the functional form of the variable 'age' might be incorrect. That is, there may be\n",
      "non-linear terms missing. The proportional hazard test used is very sensitive to incorrect\n",
      "functional forms. See documentation in link [D] below on how to specify a functional form.\n",
      "\n",
      "   Advice 2: try binning the variable 'age' using pd.cut, and then specify it in `strata=['age',\n",
      "...]` in the call in `.fit`. See documentation in link [B] below.\n",
      "\n",
      "   Advice 3: try adding an interaction term with your time variable. See documentation in link [C]\n",
      "below.\n",
      "\n",
      "\n",
      "2. Variable 'wexp' failed the non-proportional test: p-value is 0.0063.\n",
      "\n",
      "   Advice: with so few unique values (only 2), you can include `strata=['wexp', ...]` in the call in\n",
      "`.fit`. See documentation in link [E] below.\n",
      "\n",
      "---\n",
      "[A]  https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html\n",
      "[B]  https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html#Bin-variable-and-stratify-on-it\n",
      "[C]  https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html#Introduce-time-varying-covariates\n",
      "[D]  https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html#Modify-the-functional-form\n",
      "[E]  https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html#Stratification\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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9DHzZWptrucELVcfafa3Zuqy1BWPMY3gHFpatArqejDExvHFL34/3+fqaLL74/a4fW3hsmaf6CvPJ0mLVeHSDaVDRs4FdeGOmRLYMJVGypVhrU3jFHf4NwBjjxxsA/l68rkE9eIPx648oVsvjJq216Vaf0xgzgHe0/LrS0g0029FYynCLy1fVl2Kvn8x2bJnHLXmWYy2stU/jTUT5QQBjzD68Ahs/VlnkTcaYP7HWVpPY+pLFV9b49MudUagWIll8Zr6/7u/l3jPwxk4cYOF7PcDqtrPNviMrfT3NdrCaacf3bTmd+j5ON7mvLZ+VtfZDxpiDwC/hDbJ/Y+WCMeY83hnzP7LWHqc1Hf0NL1YpX/23eAVNVqKl7Z61tmiM+S68s6GH8XbeqzvweWPMI3jj1v7cWltoZd0V1fdzpb9tgD5jjGl0Fmc9VUqNfxqvGuxKLH6/67dn48s8ttn9G7F9ENkUlETJlmatLeFVq3sG7+haEPhmY8yAXWYS2Ba8l/kE6mN4ZZm/jhecc9WAa4y5gFflb8Wnkirqd4ZfgjcYeiUa7Rxs+A7AdQ2w9iLwHytHVr+/cvPrWeWZwA2w2ves/rP7C7wxAt2uvs1vwSvEsBItH4Co2Mjv45LdBiva9llZa99hjPljvAI3r8I7CxvHq572U8B9xph3W2vfudqnWG3b2ujtzCdQn8EbP/gYXsKRrXQtxRjzMF6lu1a3e1hrnzLGPKfyPN+DVyH1VrzxPUcrl18wxnyHtfbUKl9HN7yXK/EXzE+n8UG8CpJP4iU8BWutrXSDrn7HW36/V6j6O3kKb5u9Us260YpsSkqiZFuw1j5hjPkSXjB3gJuY7/Y0UblOGGNirZyNqowfqZYz/rC19oeaLN7f5L5mJur/XuXOQv0R+J0Nl1rZ/e30v5lPourLlNe/5t0b15wFWnnPYL5bV31yPom302MAZxVnHzqh/r3PrlObu/H72NbPqnKg4Dfxyvz78A60fB9eZb8E8A5jzGPW2n9Y4SoXv2fJRgvS/D2rnwJiuXHRsaVurHTj+4+Vfz8LfLNtPLXEQIPbV6Sy3o9XLhhjhvF6EvwnvO35LXjjllbaE6BqCm/b0spve6ZDZ6FuwyvoAPDr1tpfabBos/e6/vuz3OTEze6fwEtkezbJ9kxk3aiwhGwn9QOV6wNhtX+4YT5QrdTNQKDy9980WqgSBOMtrrvqq3V/t9q+qifq/n7BMssud387NfpMzjIf9F+xcc2ZZ63NA89U/n1hs2WNMTvxuvJB3XttrS0yP8j/ZaaFAW0d9DXmP4vVft+W03Xfx/X8rKy1ZWvto9baXwReU3dXs/nAFmvXezZX93fDAzuVsxqN5l8bYD6x+EijBKoyRvXWJm1pmbV2zFr7YbxxZ9UCCHcZYw61uKpqAvCcyvjHJVXmq6oWJHqi0XLrrH58W8M4w8Lxs4vVjwNeLuFstp5qPBqpdDEU2baURMm2UNkhqgYOy8KKcR9nfqfx51pcdf3Z3CWP2lb8RIvrrfcp5rtK/cziSnYrdIz5pOSHGq3DGDOCN4nkqrW481kfrGsT8tYdfQZ4sTHmxWtp0xr8W+X6kDGmWTL3H+v+/rdF9/195foAa5gYdKNYa8fxJs4EeH3lO9FuG/Z9bNG6f1bW2i8wP66tlWILDzLfVath0QtjzAtoMkFxpaJi9b1vlmx9J/NVQRdb6XbvP7JOPV4qZ4Q+XXdTq4Urqr/TGPO9CZbyeubfh8W/7Y3SjjjzFaA64e0PNtpOVw4IfVuT9fx93d8/32Q5kS1PSZRsWsaYuDHmUWPMd68gsXgXXpUn8CairXVZstY+gzeXFMBrjDH/tclzBivdSapOMZ+A/chSgakyOPqnl2lfQ5Uqd++r/HsX8IeVghmN2thrjPmZRevIM18W+Xa88uKLH+fH617XrLLgSrzDGPNbxpi9zRaqHMX89bqb/m7RIr/F/E7jh5sd9awUqlgP9zPf/emPjDHXHbk3xtyFV0QAvCIYDyxa5L3Md736X5XlGzLGvNwY88rVN7ktfq1yHcUbU9hwB9UY4zPG/NByn3e9Df4+tmLNn5Ux5g2Vap2NHvNNzA+yX/E4EWvtFeZ3YF9tjHnDEuuO482bt5xq6envMcbcssR69jK/zVnKON7cdQA/sNSZnEoy92uLb1+pynvb6ExY9UzZqyr/Lj4wthIfZP7g1P9YavtSue23K/9mgT9u8Tna5Zm6v9+41ALGmJ+kcUU9KhUI/7zy77PxqqIuXoeD9/0JN1nPJ5mvVvozxpgl21O3zhuNMf+h2TIim1anJ6rSRZfVXvC6x1Un/bsM/CHww3j95J+D1xXpPryj6tXlcsDzl1jXDuBi3XIP481N8iK8M1jfg7dTP8r1k0L+v7rH/RveuIcjeIOh/xhvgsyTeIUeLPChJZ7/7rp13L3E/cFFr+ME3lmzl+OVZX4l3lHIv8bbMZhYYh29i17jX+N1LboL70hsddLZL9ctc2AVn8tvMz+x6SfxdpC/vfKe3IVXLfH38boVVZ/ngQbr+qW6ZZJ4Y0y+tfKaX443vuSfgdNLPPZco/d70XLvrD5Hg/t/va4N5yrP+YLK9+vdlffb4iVb39FgHd9TeT8skAf+BPjeynvywsr9/w2vi5EFfnqJdVTb8M5lXs+Hqm1tcP9DrGAC1brP0eLtNP+3uvf+pXilrd+Plzha4PCixx+oe/wbl1j/hnwfW3nv2vFZVW67hrcz+sOV9+p5eGfU/hvzE54Wgee1uB04gPc7qP6+/hD45krb3og32H/Be9bgNX5L3fNcwpue4S687/Qv4W2rxvEqai75XQH+Z906voxXROP5eInN7+AlHcuto+F3BO93WcYrWvFf6r4bL6l89z5d99iPrvJ78ea6dUzgTcD74srlbZXbqvfft5rtRzsueF3Nn6hry9/gnSmsxqaPVG7/HE2+63jdMK/ULfN/6t7Xf4dX2McCX6pb5oYl1nOQ+UmULV63yh/Bi5d34X3Xf6HyGZVpsH3XRZfNful4A3TRZbUXvKNl9QFhuctFvAHQjdZ3A15/7+XW88ZFj9sHnG+y/Hm8iWfPscokqrJMHG9HcyWv9UyDddyxzHv2QbydsVXvtAJvxUscV/q5fBAINVnf24DCMus4t8TjGr7fi5Z7Z3U9De43eElfs+fPAj+0zPO8hoU7Hs0uP7zE4xvuHC1a7kON3pPK/Q/RYKd2iWV/CS+RWK69eeDQosceqLv/jQ3Wv+7fx1beu3Z8Vit8TAb4wVVuB+5mPpFa6vKu5b7TlfX8dpN1jOMlfw2/K3hJ8FebrGMSbzxjs3U0/I7Uv4ZlLg8B/av5XlSe5xdovr0qAb+42u1Huy54By+mmrTz63iFMpp+1/EOMI41Wc8H8ebsq/6/s8F6DgGPr/Az+tP1fG900aVTF3Xnk03Let0T9uAF+1/FG0NzCu/MQBnvTEe1q96bgFutNylvo/Wdxzuy94N43WYu4e2854DTwP+t3PdXix53Ee/o22/hnXHK4/U9/xreDs1zrbXfaMPrTVlrvx/vSOwH8MrbJiuvdQYvoP0JcC9eF6ml1nECb8f1N/Hemzze0dYHgf9grX1TG9r5O3jB/A143bEexdspK1aebwzviOdv4703b7Je965G6/ttvMHpv433ns7i7dhMAV/AO1P0mkaPb8Prsdban8N73/8cLznL4ZWaPwH8HnCbtfYvl1nPv+Adwf05vDOWV5j/fl3AO5r7y5V1/XmD1Wwoa+1/xysu8Ot4n+Mk3nufwvuuP4B3BnTErqJq5EZ8H1djjZ/VYbyuUv+A9/2YwHvPZvHGpfxG5XEfXmXbHsJ7z/4Q7wBNAe/M18eB11hr37HC9bwN7+zDQ5W25fG2c+/FO0P2SONHg7V2FngZ3rb3CeZ/E0/i/VafY619uPEalvVbeGf178f7nZ+vPEce7zP4u0r7j1pvnNeqWGt/C28S3T/C+05nKpdn8Lazz7HW/sbqX0Z7WGsfx0uk/gjvvSjibQMfxTvQ9ELrdflcbj1fwzuo9zs0/s0l6h4ye91KvPWcYv4M1v/F2y5mK+2q38a/wlr7o0utQ2SzM9baTrdBRERERLpAZY6zHwMuWWtXPNZRZLvRmSgRERERwRgTYb5AxRebLSuy3SmJEhEREdkGjDE3NSlv7sPrJlqtxtkV3YpFupW684mIiIhsA5Wuet+EN7b3S8BVIIJXcOLH8cY5gTdG6lVWO4kiDa3LJHgiIiIi0pVuxasq2MgXgX+nBEqkOZ2JEhEREdkGKpMr34M359sBvDkSA3iVN4/hzUH1V9Zat9E6RMSjJEpERERERKQFKiwhIiIiIiLSAiVRIiIiIiIiLVASJSIiIiIi0gIlUSIiIiIiIi1QEiUiIiIiItICJVEiIiIiIiItUBIlIiIiIiLSAiVRIiIiIiIiLVASJSIiIiIi0gIlUSIiIiIiIi1QEiUiIiIiItICJVEiIiIiIiItUBIlIiIiIiLSgq5Ioowx9xpj3meM+awxJmmMscaYv2xxHYPGmP9ojPk7Y8wpY0zWGDNrjPmcMebHjDFd8VpFRKT7KS6JiEgz/k43oOJXgOcAKWAUuG0V63g98IfAFeBB4AKwE/g+4I+BbzfGvN5aa9vSYhER2coUl0REpCHTDdtuY8xRvCB1CnglXrD5sLX2h1pYxzcDMeDj1lq37vZdwKPAPuBea+1H29l2ERHZehSXRESkma44E2WtfbD6tzFmtev4dIPbrxpj/gh4D3A3sKZgZYw5CySAc2tZj4jINnUASFprD3a6Ic1slrikmCQisiYHWGVM6ookagMUK9elNqwrEYlEBm6//faBNqxLRGRbefLJJ8lms51uRjdoV1xSTBIRWaW1xKQtn0QZY/zAD1f+/ZcWHneswV3h22+/nWPHGt0tIiKNHDlyhMcee+xcp9vRSauJS4pJIiLtt5aYtB0qA/0GcBj4J2vtv3a6MSIisu0pLomIbHJb+kyUMeYtwFuBp4A3tPJYa+2RBus8Bty19taJiMh2s9q4pJgkItJdtuyZKGPMTwPvBb4BHLXWTnW4SSIiso0pLomIbB1bMokyxvwc8D7gOF6gutrZFomIyHamuCQisrVsuSTKGPNfgN8DHscLVGOdbZGIiGxniksiIlvPpkuijDEBY8xtxpiblrjvV/EG7B4DXmWtndjwBoqIyLaiuCQisv10RWEJY8zrgNdV/t1VuX6JMeZDlb8nrLVvq/w9AjwJnMebIKu6jh8B3g2Ugc8Cb1ligsRz1toPLb5RRESknuKSiIg00xVJFPBc4EcW3XZj5QJeYHobzVVnGvYBP9dgmc8AH2q5dSIist08F8UlERFpoCu681lr32mtNU0uB+qWPbf4thWuw1hr797glyYiIpuQ4pKIiDTTFUmUiIiIiIjIZtEt3fm2lYtTGU5cniVTKBMN+rhjTy/7BqKdbpaIiGxDikkiIq1TErWBjl+a5YFjoxy/NEsyV6TsWnyOIREOcHikl3uP7OXwSG+nmykiItuAYpKIyOopidogDz41xv0PneLKbI50vkQ87CfgGLJFl/G5PFeTOU5cnuW+o4c4eutwp5srIiJbmGKSiMjaKInaAMcvzXL/Q6c4O5EmEfazeziOz5kvc1t2LZOpPGcn0tz/4Cl2xEM6+iciIutCMUlEZO1UWGIDPHBslCuzORJhP8OJ8IJgBeBzDMOJMImwnyuzOT762GiHWioiIludYpKIyNopiVpnF6cyHL80SzpfYjAearrsYDxEOl/iidFZLk5lNqiFIiKyXSgmiYi0h5KodXbisjdgNx72X3e0bzGfY4iH/SRzRU5cnt2gFoqIyHahmCQi0h5KotZZplCm7FoCywSrqoBjcK0lUyivc8tERGS7UUwSEWkPJVHrLBr04XMMRdeuaPmia3GMIRr0rXPLRERku1FMEhFpDyVR6+yOPb0kwgFSuRLlZYJW2bWkciUS4QB37FElJBERaS/FJBGR9lAStc72DUQ5PNJLLORnMpVvuuxkKk8s5OfOvZotXkRE2k8xSUSkPZREbYB7j+xld2+YZK7EWDJ33dG/smsZS+ZI5krs7g1zz117O9RSERHZ6hSTRETWTknUBjg80st9dx/i4FCMQtlyaizFpZksY8kcl2aynBpLUShbDg7FuO/oIU1qKCIi60YxSURk7fydbsB2cfS2YXb0hPjoY6M8MeqVmHWtJeJz2NkT5s69vdxz114FKxERWXeKSSIia6MkagMdHunl8EgvF6cynLg8S6ZQJhr0ccce9TcXEZGNpZgkIrJ6SqI6YN9AVAFKRES6gmKSiEjrNCZKRERERESkBUqiREREREREWqAkSkREREREpAVKokRERERERFqgJEpERERERKQFSqJERERERERaoCRKRERERESkBUqiREREREREWqAkSkREREREpAVKokRERERERFqgJEpERERERKQFSqJERERERERaoCRKRERERESkBUqiREREREREWqAkSkREREREpAVKokRERERERFqgJEpERERERKQFSqJERERERERa0BVJlDHmXmPM+4wxnzXGJI0x1hjzl6tc115jzJ8aYy4bY/LGmHPGmN83xvS3u90iIrI1KS6JiEgz/k43oOJXgOcAKWAUuG01KzHG3AQ8AgwDfw88BbwQ+FngNcaYl1lrJ9vSYhER2coUl0REpKGuOBMF/DxwC5AAfnIN67kfL1C9xVr7OmvtL1prvxn4PeBW4D1rbqmIiGwHiksiItJQVyRR1toHrbXPWGvtatdROdr3auAc8P5Fd78DSANvMMbEVt1QERHZFhSXRESkma5IotrkaOX6E9Zat/4Oa+0c8HkgCrx4oxsmIiLbkuKSiMgW1S1jotrh1sr1yQb3P4N3RPAW4FPLrcwYc6zBXavqFy8iIttO2+KSYpKISHfZSmeieivXsw3ur97et/5NERERUVwSEdmqttKZqLay1h5Z6vbK0cC7Nrg5IiKyjSkmiYh0l610Jqp6RK+3wf3V22fWvykiIiKKSyIiW9VWOhP1dOX6lgb331y5btQ3XTbQxakMJy7PkimUiQZ93LGnl30D0U43S0SknRSXNiHFJxFZia2URD1YuX61Mcapr4RkjOkBXgZkgC92onHiOX5plgeOjXL80izJXJGya/E5hkQ4wOGRXu49spfDI40O2oqIbCqKS5uI4pOItGLTJVHGmABwE1C01p6u3m6tPW2M+QRepaOfAt5X97B3ATHgA9ba9Ea2V+Y9+NQY9z90iiuzOdL5EvGwn4BjyBZdxufyXE3mOHF5lvuOHuLorcOdbq6IyIooLm1+ik8i0qquSKKMMa8DXlf5d1fl+iXGmA9V/p6w1r6t8vcI8CRwHjiwaFX3AY8Af2CMeVVluRfhzdVxEnh7+1svK3H80iz3P3SKsxNpEmE/u4fj+BxTu7/sWiZTec5OpLn/wVPsiId0xE9EOkZxaftQfBKR1eiKJAp4LvAji267sXIBLzC9jWVUjvo9H3g38BrgtcAV4L3Au6y10+1qsLTmgWOjXJnNkQj7GU6Er7vf55jK7TmuzOb46GOjClIi0knPRXFpW1B8EpHV6Iokylr7TuCdK1z2HGCa3H8ReFM72iXtcXEqw/FLs6TzJXYPx5suOxgPcWosxROjs1ycymgwr4h0hOLS9qD4JCKrtZVKnEuXOnHZG6QbD/sXdJFYis8xxMN+krkiJy43mp9SRERk7RSfRGS1lETJussUypRdS2CZAFUVcAyutWQK5XVumYiIbGeKTyKyWkqiZN1Fgz58jqHo2hUtX3QtjjFEg751bpmIiGxnik8islpKomTd3bGnl0Q4QCpXorxMoCq7llSuRCIc4I49GrgrIiLrR/FJRFZLSZSsu30DUQ6P9BIL+ZlM5ZsuO5nKEwv5uXOvZogXEZH1pfgkIqulJEo2xL1H9rK7N0wyV2IsmbvuiF/ZtYwlcyRzJXb3hrnnrr0daqmIiGwnik8ishpKomRDHB7p5b67D3FwKEahbDk1luLSTJaxZI5LM1lOjaUolC0Hh2Lcd/SQ5uAQEZENofgkIqvRFfNEyfZw9LZhdvSE+Ohjozwx6pWVda0l4nPY2RPmzr293HPXXgUoERHZUIpPItIqJVGyoQ6P9HJ4pJeLUxlOXJ4lUygTDfq4Y4/6mIuISOcoPolIK5RESUfsG4gqKImISNdRfBKRldCYKBERERERkRYoiRIREREREWmBuvNtQuqvLSIim5Hil4hsFUqiNpHjl2Z54Ngoxy95lYPKrsXnGBLhAIdHern3iCoHiYhI91H8EpGtRknUJvHgU2Pc/9AprszmSOdLxMN+Ao4hW3QZn8tzNZnjxOVZ7jt6iKO3Dne6uSIiIoDil4hsTUqiNoHjl2a5/6FTnJ1Ikwj72T0cx+eY2v1l1zKZynN2Is39D55iRzykI3oiItJxil8islWpsMQm8MCxUa7M5kiE/QwnwgsCEIDPMQwnwiTCfq7M5vjoY6MdaqmIiMg8xS8R2aqURHW5i1MZjl+aJZ0vMRgPNV12MB4inS/xxOgsF6cyG9RCERGR6yl+ichWpiSqy5247A3CjYf91x3BW8znGOJhP8lckROXZzeohSIiItdT/BKRrUxJVJfLFMqUXUtgmQBUFXAMrrVkCuV1bpmIiEhjil8ispWpsESXiwZ9+CpVjFai6FoiPodo0LfOLZPlaD4UEdnOFL+6l+KTyNopiepyd+zpJREOMD6Xr82r0UjZtaRyJXb2hLljj6obdYrmQxERUfzqRopPIu2jJKrL7RuIcnikl6vJHJOpPMOJcMNlJ1N5YiE/d+7VEaVO0XwoIiIexa/uovgk0l5KojaBe4/s5cTlWc5OpIEcg/HQkvNsJHMlDg7FuOeuvZ1r7Dam+VBERBZS/OoOik8i7afCEpvA4ZFe7rv7EAeHYhTKllNjKS7NZBlL5rg0k+XUWIpC2XJwKMZ9Rw9pw9chmg9FRGQhxa/uoPgk0n46E7VJHL1tmB09IT762ChPjHp9mV3rDcLd2RPmzr293HOX+jJ3Sv18KLuH402XHYyHODWWqs2Hoq4rIrKVKX51luKTyPpQErWJHB7p5fBIr6rqdKG1zIeiz05EtjrFr85RfBJZH0qiNqF9A1Ft2LqM5kMREVme4tfGU3wSWR8aEyXSBtX5UIquXdHyRdfiGKP5UEREZF0pPomsDyVRIm1QnQ8llStRXiZQVedDSYQDmg9FRETWleKTyPpQEiXSBtX5UGIhP5OpfNNlNR+KiIhsFMUnkfWhJEqkTe49spfdvWGSuRJjydx1R/zKrmUsmSOZK7G7N6z5UEREZEMoPom0n5IokTbRfCgiItKNFJ9E2k/V+UTaSPOhiIhIN1J8EmmvrkqijDF7gXcDrwEGgSvAx4B3WWunW1jPNwG/ADwH2AWMAceBP7DW/kubmy2ygOZDEdkaFJNkq1F8EmmfrkmijDE3AY8Aw8DfA08BLwR+FniNMeZl1trJFaznJ4H7gTTwd8AosBf4PuDbjTG/Yq19z/q8CpF5mg9FZPNSTJKtTPFJZO26JonCCzLDwFuste+r3miM+V3g54H3AD/RbAXGmADw34EccMRa+3Tdfb8OfBV4uzHmt621zUvUiIjIdqaYJCIiDXVFYYnKEb9XA+eA9y+6+x14R/DeYIyJLbOqAaAXOFkfrACstU8CJ4EIEG9Ds0VEZAtSTBIRkeV0RRIFHK1cf8Ja69bfYa2dAz4PRIEXL7OeMWAcuMUYc3P9HcaYW4CbgcdX0gVDRES2LcUkERFpqlu6891auT7Z4P5n8I4K3gJ8qtFKrLXWGPNTwF8Cx4wxfwdcBkaA7wVOAN+/kgYZY441uOu2lTxeREQ2LcUkERFpqluSqGo9zdkG91dv71tuRdbajxhjLgN/Bfxw3V3XgA8CZ1bZRhER2R4Uk0REpKlu6c7XNsaYHwI+CXwWuB2vy8XteEcL/yfw1ytZj7X2yFIXvApNIiIiy1JMEhHZmroliaoe1Ws0w1v19plmK6n0Mf9TvC4Sb7DWPmWtzVprnwLeABwDXm+MuXutDRYRkS1LMUlERJrqliSqWrXolgb3VwfkNuqfXvVqIAB8ZonBwC7wcOXfI6tppIiIbAuKSSIi0lS3JFEPVq5fbYxZ0CZjTA/wMiADfHGZ9YQq1zsa3F+9vbCaRoqIyLagmCQiIk11RRJlrT0NfAI4APzUorvfBcSAv7DWpqs3GmNuM8Ysrkr02cr1vcaYZ9ffYYx5LnAvYIFPt63xIiKypSgmiYjIcrqlOh/AfcAjwB8YY14FPAm8CG++jpPA2xct/2Tl2lRvsNY+aoz5IPAm4MuVcrLn8QLh64Ag8PvW2hPr9zJERGQLUEwSEZGGuiaJstaeNsY8H3g38BrgtcAV4L3Au6y10ytc1Y/h9TN/I/BtQA+QBD4H/G9r7YoqIYmIyPalmCQiIs10TRIFYK29iHfEbiXLmga3W+BDlYuIiMiqKCaJiEgjXTEmSkREREREZLNQEiUiIiIiItICJVEiIiIiIiItUBIlIiIiIiLSAiVRIiIiIiIiLVASJSIiIiIi0gIlUSIiIiIiIi1QEiUiIiIiItICJVEiIiIiIiItUBIlIiIiIiLSAiVRIiIiIiIiLVASJSIiIiIi0gIlUSIiIiIiIi1QEiUiIiIiItICJVEiIiIiIiItUBIlIiIiIiLSAiVRIiIiIiIiLVASJSIiIiIi0gIlUSIiIiIiIi1QEiUiIiIiItICJVEiIiIiIiItUBIlIiIiIiLSAiVRIiIiIiIiLVASJSIiIiIi0gIlUSIiIiIiIi1QEiUiIiIiItICJVEiIiIiIiItUBIlIiIiIiLSAiVRIiIiIiIiLVASJSIiIiIi0gIlUSIiIiIiIi3wd7oBIiLSuotTGU5cniVTKBMN+rhjTy/7BqKdbpaIiEhTWyV+KYkSEdlEjl+a5YFjoxy/NEsyV6TsWnyOIREOcHikl3uP7OXwSG+nmykiIrLAVotfSqJERDaJB58a4/6HTnFlNkc6XyIe9hNwDNmiy/hcnqvJHCcuz3Lf0UMcvXW4080VEREBtmb8UhIlIrIJHL80y/0PneLsRJpE2M/u4Tg+x9TuL7uWyVSesxNp7n/wFDvioU11RE9ERLamrRq/uqqwhDFmrzHmT40xl40xeWPMOWPM7xtj+lexrruMMf/HGDNaWdc1Y8xnjDE/vB5tFxFZTw8cG+XKbI5E2M9wIrwgAAH4HMNwIkwi7OfKbI6PPjbaoZZuHYpJIiJrt1XjV9ckUcaYm4BjwJuAR4HfA84APwt8wRgz2MK6fhr4MvBq4FPA7wB/B/iA17a35SIi6+viVIbjl2ZJ50sMxkNNlx2Mh0jnSzwxOsvFqcwGtXDrUUwSEVm7rRy/uqk73/3AMPAWa+37qjcaY34X+HngPcBPLLcSY8yrgT8A/g2411o7t+j+QDsbLSKy3k5c9gbhxsP+647gLeZzDPGwn2SuyInLs5uy4lGXUEwSEVmjrRy/uuJMVOWI36uBc8D7F939DiANvMEYE1vB6n4LyAL/YXGwArDWFtfWWhGRjZUplCm7lsAyAagq4Bhca8kUyuvcsq1JMUlEpD22cvzqiiQKOFq5/oS11q2/oxJ0Pg9EgRc3W4kx5jDwbOATwJQx5qgx5m3GmLcaY15ljOmW1ysismLRoA+fYyi6dkXLF12LYwzRoG+dW7ZlKSaJiLTBVo5f3dKd79bK9ckG9z+Dd1TwFrz+5I28oHI9BjwEvGLR/U8YY77PWntquQYZY441uOu25R4rItJOd+zpJREOMD6Xr82r0UjZtaRyJXb2hLljT/dXN+pSikkiIm2wleNXtxwFq75Tsw3ur97et8x6qoXlfww4AHxHZd23AH8J3Al83BgTXG1DRUQ22r6BKIdHeomF/Eym8k2XnUzliYX83Ll3c84A3yUUk0RE2mArx69uORPVLtWk0Ad8v7X2C5X/k5UysrcBzwfuAf6q2YqstUeWur1yNPCu9jRXRGRl7j2ylxOXZzk7kQZyDMZDS86zkcyVODgU45679nausVKlmCQi295WjV/dciaqelSv0bm76u0zy6ynev/VumAFgLXWAn9f+feFLbZPRKSjDo/0ct/dhzg4FKNQtpwaS3FpJstYMselmSynxlIUypaDQzHuO3poU0xU2MUUk0RE2mSrxq9uORP1dOX6lgb331y5btQ/ffF6ZhrcP125jqysWSIi3ePobcPs6Anx0cdGeWLUKxvrWkvE57CzJ8yde3u55669myYAdTHFJBGRNtqK8atbkqgHK9evNsY49dWQjDE9wMuADPDFZdbzRbzSsweMMTFrbXrR/Ycr12fb0GYRkQ13eKSXwyO9XJzKcOLyLJlCmWjQxx17Nkcf8k1CMUlEpM22WvzqiiTKWnvaGPMJvGpHPwW8r+7udwEx4AP1AcgYc1vlsU/VrSdjjPkT4C3AfzPG/OdKlwmMMXcCbwRKwAPr+4pERNbXvoHopgw6m4FikojI+tkq8asrkqiK+4BHgD8wxrwKeBJ4Ed58HSeBty9a/snK9eJaib+KV0b254CXGGM+D+wEvg8IAz9nrT29Hi9ARES2DMUkERFpqFsKS1AJIs8HPoQXqN4K3AS8F3ixtXZyhetJAi8Hfh0YAH4a+E7gc8C3WWvf2/bGi4jIlqKYJCIizXTTmSistReBN61w2YazdVlrU3hHCRcfKRQREVkRxSQREWmka85EiYiIiIiIbAZKokRERERERFqgJEpERERERKQFXTUmajvaKrXyRURk81NMEhFZGSVRHXL80iwPHBvl+CVv1uaya/E5hkQ4wOGRXu49srlmbRZpJ+3IiWwsxSSR1ihOiZKoDnjwqTHuf+gUV2ZzpPMl4mE/AceQLbqMz+W5msxx4vIs9x09xNFbhzvdXJENox05kY2nmCSycopTUqUkaoMdvzTL/Q+d4uxEmkTYz+7hOD5nvjJu2bVMpvKcnUhz/4On2BEP6cco24J25EQ2nmKSyMopTkk9JVEb7IFjo1yZzZEI+xlOhK+73+eYyu05rszm+OhjowpYsuVpR06kMxSTRFZGcUoWU3W+DXRxKsPxS7Ok8yUG46Gmyw7GQ6TzJZ4YneXiVGaDWijSGYt35OoDE8zvyCXC/tqOnIisjWKSyMopTsliOhO1gU5c9vrPxsP+6358i/kcQzzsJ5krcuLyrAYrypZVvyO3ezjedNnBeIhTY6najpx+FxtPg6m3DsUkkZVRnOpenYxJSqI2UKZQpuxaAssEq6qAY3CtJVMor3PLRDpHO3KbgwZTbz2KSSIrozjVfbohJimJ2kDRoA9fZQDiShRdS8TnEA361rllIp2jHbnup8HUW5NiksjKKE51l26JSUqiNtAde3pJhAOMz+VrGXMjZdeSypXY2RPmjj06uitbl3bkupsGU29dikkiK6M41T26KSapsMQG2jcQ5fBIL7GQn8lUvumyk6k8sZCfO/dqvIFsbdUduVSuRNm1TZet7sglwgHtyG0QDabeuhSTRFZGcap7dFNMUhK1we49spfdvWGSuRJjydx1P8ayaxlL5kjmSuzuDXPPXXs71FKRjaEdue6l6m1bn2KSyPIUp7pDt8UkJVEb7PBIL/fdfYiDQzEKZcupsRSXZrKMJXNcmslyaixFoWw5OBTjvqOH1C1GtgXtyHWntQymls1BMUlkZRSnOq/bYpLGRHXA0duG2dET4qOPjfLEqPeFcK3Xf3ZnT5g79/Zyz12qdCXbR3VHrjpQ9NRYqjZQtFjpGhEL+bUjt8E0mHp7UEwSWZ7iVOd1W0xSEtUhh0d6OTzSqzlXRCq0I9d9NJh6+1BMElme4lRndVtMUhLVYfsGogpQm4B2LDaGduS6i6q3bT+KSZuDtpGdozjVOd0Wk5REiTTRDZO5bUfakesO1cHUV5M5JlN5hhPhhstqMLXI+lNM6h6KUxuv22KSkiiRBrplMjeRTrr3yF5OXJ7l7EQayDEYDy05J0cyV+LgUEyDqUXWiWKSSHfFJCVRIkvopsncRDpJg6lFOk8xScTTTTFJSZTIEhZP5rZYdTI3yNUmc1PAkq1Kg6lFOksxSWRet8QkJVEii9RP5rZ7ON502cF4iFNjqdpkbuofLVuVBlOLdIZiksj1uiEmKYnaRrTzszJrmcxN76dsdRpMLetB8akxxSSRxjoZk5REbQOq5tOabpvMTURkq1J8Wp5ikkh3UhK1xamaT+u6bTI3EZGtSPFpZRSTRLqTkqgtTNV8VqfbJnMTEdlqFJ9WTjFJpDs5nW6ArJ/F1XwWb3ir1XwSYX+tmo/MT+YWC/mZTOWbLqsJRkVEWqf4tHKKSSLdSUnUFlVfzWcwHmq67GA8RDpfqlXzEW8yt929YZK5EmPJHGXXLri/7FrGkjmSuRK7e8OaYFREZIUUn1qnmCTSfdSdb4tSNZ+16eRkbqpSJSJbmeJT67ppglFQnBIBJVFblqr5rN1GT+a20ipVCl4ispkpPq1ON0wwulycevnNQxTLruKTbAtKorYoVfNpj42azG0lVaoePTvJnr4IM5miSgGLyKal+LR6nZxgtFmcujKb46mrSR44dpHeSJBwwFF8ki1PSdQWpWo+7bWek7mtpErVxakMJ6+lODWeJuR36IsGVApYRDYlxae12+gJRpvFqelMgel0gVzRpexaCuU8O+IhHAfFJ9nSVFhii1I1n81juSpVuWKZdL6MtRbXtUQDPkb6Igwnwoz0RTg0HCfoM7VSwMcvzXbolYiILE/xafNpFKfS+RKXp3PkSy5+xxD0Gwxe8qv4JFtdVyVRxpi9xpg/NcZcNsbkjTHnjDG/b4zpX8M6X2GMKRtjrDHmv7Wzvd1O1Xy630qqVI3P5SmUXfw+7+eaKZTJ13WDUSlgkfWhmLR+FJ82j2ZxqhqffMYQ9DsE/T7KriWdL5EvuopPsqV1TRJljLkJOAa8CXgU+D3gDPCzwBeMMYOrWGcP8GfAtqyLWq3mc3AoRqFsOTWW4tJMlrFkjkszWU6NpSiU7YZV85HrLVelKlcsk8p7g7CDPoPPMZRcS7pQum5ZlQIWaR/FpPWl+LR5NIpT9fEp4PNuN7BknFJ8kq2om8ZE3Q8MA2+x1r6veqMx5neBnwfeA/xEi+t8L9AL/PfK47edbqjmI40tV6Wqer/PMRhjMMYC9rqjtqBSwCJtppi0zhSfNodGcWpxfKry/lwYpxSfZCvqiiSqcsTv1cA54P2L7n4H8GbgDcaYt1pr0ytc5/fgHUF8A13yOjulk9V8pLnlqlSVXYvFUg1P1oIxpuFAbJUCFlk7xaSNo/jU/RrFqcXxqapRnFJ8kq2mWzbkRyvXn7DWLviVWmvnjDGfxwtoLwY+tdzKjDHDwP8GPmat/UtjzBvb3N5NaaOr+cjylqtS5XMMBoOLxeIFrWDAIRZc+qerUsAibaGYtMEUn7pXozhVH5+qmsUpxSfZarolibq1cn2ywf3P4AWsW1hBwMILVg6td7WoMcYca3DXbatdp8hi1SpVo9NZLkxman3OY0E/oYBTOwJYLLoUSmXvvpB332IqBSzSNopJsq0tPjN4YCjG1WSOyVSe4UQYYEF8stZijKFYdpeMU4pPshV1SxJV/UU1qn1Zvb1vuRUZY34U+G7g31trr629abLetnM3juOXZplM5Unli2SLLpOZAn5jCPgcYiE/O3pCRIM+csUypbIlGvKxo2fpKn4qBSzSNopJ29h2j0kPHBvl+KXZBZO6+4yhUHJJ5bwTs4PxEOGAj3jIR7HsUihbjPHGQYUD18cpxSfZiroliWoLY8wB4PeBj1hr/+9a1mWtPdLgOY4Bd61l3eJptLHeLjOc18/+Dl7XCNe1FFyXQtklWywzlSngMwbHGFwgFvQRXnQWquxaJlN5krkSB4diKgUs0iUUkzYXxaT5mJTOl4iH/bVJ3VO5Eo5jKLkuyVyJ6cx8tT4LFEveGahwwGGkL0Is5HXZU3ySraxbkqjqUb1GW6fq7TPLrOdPgSxwXxvaJOuo2cZ6O8xwvnj299t3J8gVy4zP5SslY11Krq30LTc8e28fqXyJ2WyRU2Op2vtVrHSRiIX8KgUs0j6KSduMYtLCmLR7OL5gjG41GZqqzAk13Bui5FpcaxmKB0lmi4DBGEgXShRKZcUn2fK6JYl6unJ9S4P7b65cN+qfXnUXXnAbry+3Weftxpi3A39vrX1dq42U9ljpxro6w/mOeGjLbXgXz/4OEAv5iYX85IrlWunYVK6IMYZn7Ulwz117VQpYZGMoJm0jiklLx6R61UlzAQply7P2JPiW23fWujz6HYfPn55QfJJtpVuSqAcr1682xjj11ZAqkxO+DG9ywi8us54/B5bqbHsz8ArgcbzJE7+61gbL6q18Y52rzXC+lTa+9bO/7x6OX3d/OOAjHPC6QgzEgpwaS/HE6Cw/+rKDvOO77tjW/fVFNohi0jaimNQ8JtUbjIc4NZbi3ETmutjzLc/aqfgk20pXJFHW2tPGmE/gVTv6KeB9dXe/C4gBH6ifj8MYc1vlsU/VrectS62/Uk72FcDHrbW/0vYXICu2mo11dYbz1W6Iu22j3mj296UsNUGhSgGLrC/FpO2jEzGp1fatd/xaa0yqp/gk20lXJFEV9wGPAH9gjHkV8CTwIrz5Ok4Cb1+0/JOV6+a/eOkq7dxYL6dbBwk3mv29EU1QKNIRiknbwEbGpFZsZPxSTBJZna5JoipH/p4PvBt4DfBa4ArwXuBd1trpTrZP2mOjNtbdPEi40ezvjWiCQpGNp5i0PXRjArHR8UsxSWR1uiaJArDWXgTetMJlV3y0z1r7IeBDq2uVtNNGbKy7fZBwo9nfl6IJCkU6RzFp6+u2BKIT8UsxSWR1nOUXEWmf6sY6lStRdm3TZasb60Q40NLGevEg4cUBoTpIOBH21wYJb6R9A1EOj/QSC/mZTOWbLqsJCkVE1s9GxKRWdCJ+KSaJrI6SKNlQ672xrh8kPBgPNV12MB4inS/VBglvpHuP7GV3b5hkrsRYMndd8C67lrFkjmSuxO7esCYoFBFZB92UQHQyfikmibROSZRsuPXcWK9lkPBGOjzSy313H+LgUIxC2XJqLMWlmSxjyRyXZrKcGktRKFtNUCgiss66JYHoZPxSTBJpXVeNiZLtobqxrg6cPTWWqg2cXesM5904SLiRo7cNs6MnpAl0RUQ6aD1jUis6Hb8Uk0RaoyRKOmK9NtbdNkh4OYdHejk80tt1c1mJiGwn3ZBAdEP8UkwSWTklUdIx67Gx3qxVhjRBoYhIZ3U6geim+KWYJLI8JVHSce3cWFcHCV9N5phM5RlOhBsuqypDIiKyWKcSCMUvkc1FhSVky+mWQcIiIiKtUPwS2TyURMmWoypDIiKyGSl+iWwe6s4nW1I3DBIWERFpleKXyOagJEq2rE4PEhYREVkNxS+R7qckSrY8VRkSEZHNSPFLpHtpTJSIiIiIiEgLlESJiIiIiIi0QEmUiIiIiIhIC5REiYiIiIiItECFJUTaQBWURERks1DMElk7JVEia3D80iwPHBvl+CVvLo+ya/E5hkQ4wOGRXu49ork8RESkOyhmibSPkiiRVXrwqTHuf+gUV2ZzpPMl4mE/AceQLbqMz+W5msxx4vIs9x09xNFbhzvdXBER2cYUs0TaS0mUyCocvzTL/Q+d4uxEmkTYz+7hOD7H1O4vu5bJVJ6zE2nuf/AUO+IhHd0TEZGOUMwSaT8VlhBZhQeOjXJlNkci7Gc4EV4QjAB8jmE4ESYR9nNlNsdHHxvtUEtFRGS7U8wSaT8lUSItujiV4filWdL5EoPxUNNlB+Mh0vkST4zOcnEqs0EtFBER8ShmiawPJVEiLTpx2RuQGw/7rzuat5jPMcTDfpK5Iicuz25QC0VERDyKWSLrQ0mUSIsyhTJl1xJYJhhVBRyDay2ZQnmdWyYiIrKQYpbI+lASJdKiaNCHzzEUXbui5YuuxTGGaNC3zi0TERFZSDFLZH0oiRJp0R17ekmEA6RyJcrLBKWya0nlSiTCAe7Yo0pHIiKysRSzRNaHkiiRFu0biHJ4pJdYyM9kKt902clUnljIz517NRu8iIhsPMUskfWheaJEVuHeI3s5cXmWsxNpIMdgPLTknBvJXImDQzHuuWtv5xq7RVycynDi8iyZQplo0McdexTkRURWQjGr/RSTREmUyCocHunlvrsP1WZ/PzWWqs3+Xqx0h4iF/BwcinHf0UOatHANjl+a5YFjoxy/5FWYKrsWn2NIhAMcHunl3iN79f6KiDShmNU+iklSpSRKZJWO3jbMjp4QH31slCdGvY2pay0Rn8POnjB37u3lnru0MV2LB58aqwX9dL5UC/rZosv4XJ6ryRwnLs9y39FDHL11uNPNFRHpWopZa6eYJPWURImsweGRXg6P9Oq0/jo4fmmW+x86xdmJNImwn93D8SW7n5ydSHP/g6fYEQ8p+IuINKGYtXqKSbKYkiiRNtg3EFUAarMHjo1yZTZHIuxnOBG+7n6fYyq357gym+Ojj40qYImIrIBiVusUk2QxVecTka5zcSrD8UuzpPMlBuOhpssOxkOk8yWeGJ3l4lRmg1ooIiLbhWKSLEVJlIh0nROXvf768bB/QXeJpfgcQzzsJ5krcuLy7Aa1UEREtgvFJFmKkigR6TqZQpmyawksE6yqAo7BtZZMobzOLRMRke1GMUmW0lVjoowxe4F3A68BBoErwMeAd1lrp1fw+BjwOuA7gLuAfYALPA38FfA+a21hPdouW0+nB952+vk7KRr04atUPFqJoutVmIoGfevcMtlOFJOkW3U6PnT6+TeaYpIspWuSKGPMTcAjwDDw98BTwAuBnwVeY4x5mbV2cpnVvBz4S2AKeBAv2PUD3w38NvB9xphXWWtz6/IiusR227i1W6fngOj083eDO/b0kggHGJ/L115/I+XKHCc7e8LcsWdrvy+ycRST1ofi09p0Oj50+vk7RTFJltI1SRRwP16weou19n3VG40xvwv8PPAe4CeWWcdV4IeAj9Qf3TPGvA14CHgp8FPA77S15V1iu27c2qnTc0B0+vm7xb6BKIdHermazDGZyi9ZCalqMpUnFvJz517tjElbKSa1keLT2nU6PnT6+TtJMUmW0hVJVOWI36uBc8D7F939DuDNwBuMMW+11qYbrcda+zjw+BK3zxljfgf4MHA3WzBgbeeNW7t0eg6ITj9/t7n3yF5OXJ7l7EQayDEYDy35fiRzJQ4Oxbjnrr2da6xsKYpJ7aX4tHadjg+dfv5uoJgki3VLYYmjletPWGsXdDi11s4BnweiwIvX8BzFynVpDevoSvUbt6DPcGg4zkhfhOFEmJG+CIeG4wR9prZxO35J1WKWsngOiMWn66tzQCTC/tocEFvp+bvN4ZFe7rv7EAeHYhTKllNjKS7NZBlL5rg0k+XUWIpC2XJwKMZ9Rw9tuYAtHaWY1CaKT+3R6fjQ6efvBopJslhXnIkCbq1cn2xw/zN4RwVvAT61yuf40cr1v6xkYWPMsQZ33bbK5183mgBu7erngNg9HG+67GA8xKmxVG0OiHacru/083ero7cNs6MnxEcfG+WJUa8bkGu9Abs7e8LcubeXe+5SNyBpO8WkNlF8WrtOx4dOP383UUySet2SRFW/bY0OQVVv71vNyo0xP41XXelx4E9Xs45upY1be6xlDoh2vI+dfv5udnikl8MjvRqQLhtJMakNFJ/ao9PxodPP320Uk6SqW5KodWOM+T7g9/EG+N5jrS02f4THWnukwfqO4ZWq7QrrvXHbLhuJTs8B0enn3wz2DUS35HdPtpetHpPqKT61R6fjQ6efv1spJkm3JFHVo3qNzn9Wb59pZaXGmNcBfw2MAUettWdW07hutl4bt+1WSanTc0B0+vlFZAHFpDZQfGqPTseHTj+/SLfqliTq6cr1LQ3uv7ly3ah/+nWMMa8H/g/e0b5vttY+s/rmda/12Lhtx0pKnZ4DotPPLyILKCa1geJTe3Q6PnT6+UW6VbdU53uwcv1qY8yCNhljeoCXARngiytZmTHmB/Fmg78MvHIrB6vqxi2VK1F2bdNlqxu3RDjQcOO2XSspVeeAiIX8TKbyTZddjzkgOv38IrKAYlIbKD61R6fjQ6efX6RbdUUSZa09DXwCOIA38WC9dwEx4C/q5+MwxtxmjLmuKpEx5keAPwcuAK/Y6t0l2r1x285lTO89spfdvWGSuRJjydx1Qb/sWsaSOZK5Ert7w22fA6LTzy8iHsWk9lB8ap9Ox4dOP79IN+qW7nwA9wGPAH9gjHkV8CTwIrz5Ok4Cb1+0/JOV69pW1BhzFK/SkYN3JPFNxlx32nnGWvv77W58J7VrArjtXkmpOgdEtavIqbFUratIsXKUNBbyr9scEJ1+fhFZQDGpDRSf2qPT8aHTzy/SjbomibLWnjbGPB94N17p19cCV4D3Au+y1k6vYDU3MH927UcbLHMerzLSltGujdtqKyl95uQYQ/HQlqiQ1Ok5IDr9/CLiUUxqj07Hp2qlv61Qya/T8aHTzy/SbbomiQKw1l4E3rTCZa/bilprPwR8qL2t2hzasXFrtZKS61quJXP8yefOEvA5W6ZCUqfngOj08zfSbe0RWW+KSe3RifhUrfR38tocXzwztWUq+XU6PnT6+RvptvbI9tBVSZSszVo3bq1UUprOFBhPFXBdS6mcZyAe3HIVkjo9B0Snn79qu5UTFpH228j4BF6lv3LJ5R++dplc0d1ylfw6HR86/fxVik/SSUqiNrlGAWk1G7eVljFN50tcms5SLLkE/Q6HdsSJhOZL0lb7uFcrJO2Ih7QR26S2YzlhEek+rZbZnskUsdaSLZTpjwbYPRxfciyW4tTmpfgknaYkapNaj6Mv1UpKV5M5JlN5hhPhJZcbn8uTK7r4HEN/NLgggYL5CkmQq1VIUnDafOrLCSfCfu2EiMiqrTVmrTQ+gVfpz1oLGPqjgSWXVZza3BSfpBt0RYlzac2DT43xrn88wSefvMbp8RTZYtk74lYsc3o8xSefvMa7/vEEDz491vK6lytjmsmXmM54ATAccNjRE2q4rsF4iHS+VKuQJJvLdi4nLCLt066YtdIy29OZIgDGeHGoGcWpzUnxSbqBkqhNZr0nG6xWUjo4FKNQtpwaS3FpJstYMselmSynx9Pe4F6/w97+KLFQ45nlF1dIks2jvpywdkJEZLXaGbOWi0+nxlIUypaBWJDeSJC+aKDlSn7S/RSfpFuoO98Gcl0Xx1lb3rr46Mti7eii0KyS0nBPiKlMkXjIT180sOy6qhWSMoVyS22QzlprOWEREWh/zFpJpb/+aJC/f/xSpUvf8hSnNhfFJ+kWSqI20Kc+9SlCoRCDg4MMDQ0xMDBAILB8IlK1kZMNNqqkNJEq8OdfOEe2uLJgU3S94BYNNj5jJd1nteWEtRMiIlXrFbOWq/T3L8evtFzJT3Fq81B8km6hJGqDpNNpcrkcuVyO2dlZzpw5gzGGRCLB0NAQg4ODDA4O4vc3/kg6cfRlcaW/i1MZPvbVSyuukJTKldjZE+aOPd07oLM+EGcKJcAQDfq29VwTqyknrJ0QEam33jGrUSXaViv5dXOcevTsJJ968hpzuRI9YT+vun0nLzw42OlmdZTik3QLJVEbJJlMYoxZ0L3AWstTTz3F+Pg4iUSCRCLBvn372L17d+1Mlc83/6PvhqMvrVZIioX83Lm3OxOR+mpRY3M5ZrNF8iVvoxzy++iLBNjRE9qWc01spZ0QEemMTsWsrRCnPvbVS3zg4dNcnMpQKFksFoPhw1+6wL6BKP/pFTfxuueNdLqZHaH4JN1CSdQG2b17N9/2bd/G1NQUk5OTTExMkEwmSSaTtTNUY2NjnDp1ikgkQk9PD729vezbt489e/YwNDRE2G+64ujLvUf2cuLyLGcn0kCOwXhoydKiyVyJg0Mx7rlrb1ufvx3q55eYyRQolC2ua3ErSW6p7FWOmkwXtuVcE1thJ0TWZrWToopUdfKMwWaOU7//yZP88WfPkC26uK7FcQzGgGst6bzLyWspfuVjT3B+Ks3PvuqWTjd3wyk+Sb1OxiolURsoEAiwc+dOdu7cCUChUCCVShGJREgmk2QyXuWYbDZLNptlbGyMZ555hnA4TE9PD24gSv5inslylAH/HsKxHoxZ+gjMeh59qVZIqiYhp8ZStUnuipXnjYX8HByKcd/RQ113Bqe+WlTI7xX6sNbidwwBnwPGUCy7lfK5FmvZlnNNbOadEFm99ZiDTranTp4x2Kxx6mNfvcQff/YM6UIZn2OIhXw4dXHetZZcySVdKPO/Hz7DDQOxbXlGSvFJuiFWKYnqoGAwyOtf/3qmp6eZmJjg2rVrXLx4kZmZGZLJJNlsFqB2pgrAjo6TninwjfNRBgcGiA7sIto/TCjeRyASq617vY++rKRC0j13defOVn21qEyhTLFs8RlD0D9fOTHocyjgUixbyq5dMNdEN76m9bDZdkJ05mTt6s/QpvOl2uedLbqMz+W35VlZWb1OnzHYjHHqAw+fJluZzD4auP6MnGO82zPFMtmiy/96+PS2TKI2S3xSXFof3RKrlER1mM/nY2hoiKGhIW677TbK5XItqbpy5cqCpCqfz3P7nh7GU+PMzExTTM0SuXoBnz9AIBwlFO8l3L+LfKifvD/OTXsG1/Xoy3IVkrpRfbWogf4IY3MFyq4lEri+9HzA55AtlEnnS+zsiXFhOrPqaoeb1WbYCemGo1FbQf0Z2kTYz+7h+JJHdrfjWVlZvU6fMdhMcerRs5NcnMrgurbpHIwAYb9DOl/mwlSGR89ObstiE90cnxSX1k83xSolUV2mUVI1OTnJ5cuXOX/+PP7IKA8dP0cynWM6XSTgL+HL5ChPTlI8fZpgwE9fb4JDsVuYOl1i1N7I8PAwwWBwXdrcqEJSN6qvFpUrubUN21LdIg1exaiSa8mWytt2rolu3gnplqNRW8FGzEEn3SWZTBIMBgmHG58hWqtuOWOwGeLUp568RqHkjYFyGnTVr3KMwXEMhZLlU09e25ZJFHRnfFJcWl/dFKuURHW5+qTq1ltvxXVdvmt6mi984xwPPPw1jj99huTsNKVcGse6xIIOOxNhbtsdxp8a49/+7d8wxhCLxdi1axcHDhzgpptuYteuXU3LqW9V9dWiym614lFjXhyztcds57kmum0npJuORm12GzkHnXSPr3/960xPTxOJRBgYGKCvr4+BgQESicSaJ4av181nDLrJXK7kxaSVFTPEGLBY5nKl9W3YJtAt8UlxaX11W6zafnvRm5zjOAwODvKdLx/kO19+hAuTab58cpRrY+PMTV7Fn53CZpPMzc3hul5FJGstqVSKU6dOcerUKT75yU8SDofZs2cPN9xwAzfeeCP79u1bUE59Od101KcV9dWiIgEHg8Gl8az21oIx81URNddE9+imo1GbXSfmoJPOcl2X2dlZwCtmdOnSJS5dugR4caavr4/+/v7aZa1nq9b7jMFmjUn1esJ+LybZxjGpnrXeGamesHbluoXi0vrqtlilX94mt38wxv6X3ArcWrstnU4zPj7OuXPnOH/+PFevXiWdTi+YoyqXy3HmzBnOnDnDgw8+SCAQYGRkhP3799eSqqW6/232fr711aL6IwF8jqFYdLHWXtelz+IdNQoGHCJ+H+Nzec010SW67WjUZtcNc9DJxsrn8/T39zMzM0O5vPBzdF2Xqakppqamare162xVu88YbPaYVO9Vt+/kw1+6QDrv4lrbtEufa71pOSIhh1fdvnMDWymNKC6tv26LVUqitqBYLEYsFuPAgQOAV0p9bGyMs2fPcu7cOS5dukQ6nV7wmGKxyLlz5zh37hwPP/wwfr+fXbt2sX//fm666Sb27NnDl0czG9bPd72OKtZXi0rlS8RDPoplrwpf0L/wR1ksu5USs37m8kXNNdFFuu1o1GbXyfl8pDMikQgvfelLcV2Xubk5pqammJ6eZnp6ujbdRr2NOFvVqk6NPVmv+PTCg4PsG4hy8lqKXMldsjpfVa7k4jiG/QPRbTseqtsoLq2/botVSqK2gWAwyN69e9m7dy8vf/nLcV2Xrz59ns985QkuXrjA7ORV4k6J3kig9phSqcTo6Cijo6M88sgjzOQtn7+YI+nvZWDnCPt27SYcj+I43hezXf18N+KoYn21qJDfIeAz5EsuhZJLwGcWzBMV8jv4HKO5JrpMtx2NWg8b2T2pk/P5SGc5jkNvby+9vb0cPHgQ8M5SVROq6enpls9WVZOqVs5Wtfp978TYk42IT//pFTfxKx97gnShTKZYJux3lpwnquxaYkEfb37FTWt6PmmfrR6XuqHLbLfFKiVR28zCINBLOXwY3747CZUy7DKz3BrLQnqSZDK5oPvf185NMD6RwudcIzV1hnOnwwQiMaL9w8QGdxPq6WcwlgBYdT/fjTqquLhaFHjjnkqupVD2jm5UKx+BN1N8p+eakIW67WhUu1ycyvDPT1zhs6cmuDyTxbXgGNa9e1Kn5/OR7hIKhdi1axe7du0CqJ2tmp6erp2xWsnZKp/PR29v74LEKhQKLXjMahOTjR57slHx6XXPG+HcZJo//uwZskWXdL6M43hxyFpwXa96Xyzo48dfceO2nCOqW23FuNSpmNRIt8UqJVHbSNMgkPMxHRpmxoT5ye+8ieftDNbGTB0/eYbx9GWKZUu8MoC1VMhRKuTIzk4yde4p/KEI/kiUcGIHE/TwqDvHM3ft4uaRlXUz2OijiourRY3N5ZjNFsmXvI1fyO+jLxJgR09IlaO6ULcdjVqr6o7k505NMDqdoVS2uNbidxzCAYdo0L/upXE7PZ+PdK/6s1XVbuIrOVtVLpevO1sVjUbp7+9nYGCAJ8ZL/NlXrnE1mW8pMdnosScbHZ9+7ltu4cBgjP/18GkuTGUolLxKso4xREIO+weivPkVNymB6jJbKS51Q0xqpJtilZKobaKVIPCHD53mHd91B0eOHOHIkSPEvn6Jh+1jhMevEM1Pkp0Zp5hLUy4WAK/EajGfoZjPkJ2ZIJsv88ylOP8zf5JX3XUre/fuZXBwkMHBQeLxpQNeJyraLFUtKlMoAYZo0LdpKzxtB912NGotqgc3zk9mmEwXsNY70uw3XlePfMnFUmJ3b5hS2V230rjdMp+PbA6NzlYtN7Yqk8mQyWR45Osn+atHLzCWKpJI9DI8NEjESRCMJXB8/qaJyUaPPelEfHrd80Z43fNGePTsJJ968hpzuRI9YT+vun2nxkB1qa0Sl7olJjXSTbFKSdQ2sZYgkCtZnEgP/fviDCfuxC2XKKSTZGcnSE1cJjczSTGfoZzPYgEHSzE7x6XRUb5cTvL444+TSCQo+cKknSjRviGGd+zgRbfdwG37h9t6VHE1fXa7ZX6J7aQdfas7cTSq3X3C6w9uFEpeIRMHQ9DvjSOxeAVOcsUyV2ZzHBiMAavvMrsczecjq7XU2KpcLnfd2arq1BufPzXJdLpAxG+I2iyZ8VEy4966AuEogWgPsVgPJRO67vvezrEny/2mOx2fXnhwUEnTBlvLdr6TZ0naEZ+6LSY10i2xSknUNrDWILC4n6/j8xNODBBODNC/7xbKxQL51Cy55BTpycvkr43hK+cJVSoLXZqa45PHLzM2l6NQdjG+IIFIjJ6eXm49sIcD+/YwdjVPNNiz6qOKW6nM7VbWzs9pI49Grdf3q3pwIxJwyBVdXNcSCswPxDdA0OdQwCt8Mj6XZ99AZF1L4673fD6yfYTDYXbv3s3u3buB+bmpTpy9zFh5nAJJ+sPXjwcp5jIUcxkyU9dwreVqqsRnpoZ5eFeZO24cIeRjzWNPVvqbbsdZL8WnzaEdn1MnzpK08/vVjTGpkW6IVUqitoG1BoHl+vn6AkGi/TuI9u+gd98tzI1OMBIu8e9fNMRj33iGr5w9yVQasrkyAcfic/MUCjlmJie4OnqWL4QjuIEokVgPwZ07CfX0Ee7pr3XrWGzxUcVOlbmV1qzH57QRR6PW6/tVf3BjIBak7BbxOea6+coAAj6HbKFMOl+iVBmbuN6lcXWGVtrNcRz6+/tJXcrhG76J3f0H2Bl1KGaS5NNJipk5itmFcxo6xhByXKYnx/n0l77K7NXzTKQKFEavMZ7302t3EYkn8AVCSz7nUmNPWvlNr/Wsl+LT5tDOz2kjz5K0s93dHpMa6WSsUhK1Daw1CLTazzfRE+fFz9rJjc/ay5+f8lG+YYCdIykiboZ8cpxiOkUxl6aQTZPJF0nOpXDtHIXZCZzkFQKRWOXSQ6R3kFBPP+FEP4Gwd9q4/qhiJ8rcSuvW83Naz6NR69nu+oMb4I0tbPQLNXgHOEquJV0obbrSuCL16mOSPxjCH9xBpG8HAK5bpphJUUgnKWTmKGSS+J1CbSwGwFA8yO6IZWxijAtPj9MbCeIPhgnGegjGvHFV/lAUY8x1Y09a/U1/6+07V33WS/Fpc1iPz2kjzpK0u92KSa1TErUNtKPs5mr6+T5wbJSryTwD/X0MJ6qDj8sU0knyczPk5qbJzU4yPjnNTDJJrpClXC7hpr2jknCF5NUgwUicQCRGqKefYM8A0/kQO27Ywx17evmTz53d8AG/0rqNGJi9Hkej1rPd9TuSPsdgMLjYhst7BwMtZdduitK4Io00i0mO4yMU7yUUn/8dFa9N4S9m2Levn3jcRyqV4mWHBrkwleZaMgcU6LGWUiFHZroysMrxkTFh8r4oN47s5HXP8boUtvqbPjk2t+qKa4pPm8N6bufX8yxJu9utmNQ6JVHbQDvKbrbaz7c3ElhyHJbj+Aj39BPu6aeXg5SLBQbnZnji1EWKqWnKNkOPU6SYTVEq5ikXC2SLU2STU5ixUXImhAlGCfj38tiXXb70tQnm8kFu2TvU9D1Ya5nbYrFIPp8nm82Sy+VwXZeenh4SiQR+v35GzWx0OeJ2We921+9IJiLe38Wii7V2ye4T1nrzmRlDV5fGFVlOqzEpawPctHuE7zr6XPYNRCkUCkxPT9MzfIoPPXicaxOTXJnNEQ44+CtHx3NFl7A/Q38sxYtiPi5+/RGeeSLEI1++xkzOxy37dzdtY/U3fW4iw4GhWMsV14BNud3bbhSf5ikmtU57f9tAu8puttLP91+OX1nROCxfIEh8YJhdN8aYzRZxSjmCZOgtpynMTVJIpyhmU+SzKdK5ArlinkggxczFHO//8FnOzRlMKMpEZoRI3yDhxCCBSPy6H/xSY71c16VQKJDL5cjn8wsu9bflcrnr5j+pF4vF6O3tJZFIEAwGcRwHYwyO4+A4Dn6/n1gsRjgcXnJD1M3a0RVho8sRt8t6t7t+R3JXIkw85KNYdimWLUH/wuezeDuTwYBDvuh2bWlckZVYa0wKBoPs3LmTH3ntTo485zAf+coFvnrqElPT0xTSScrZOfoiLgeGYrz0pkFuGIxhreX4ucuMX7mMWyoznhnFHwrXuv8Fown8oUhtG13/m75lOM75yfSKe2J806Eh/vKL5xidznpdnsrNE8Vu2u51u3Z3j1N8mqeY1DolUR1w+fJlzp49C1AbQGutXfB3s+uq+v8X37fYSCaNc/kco8kcU0E/iUgAnzFUO7y6LiRzRTJFl129YfbflOXhhycrRxnmL47j8Jphw13hEmenshRKlkjIz807w+zptzizl3g6dZVTp8aYGxsF45BxIxjHwTg+79o4GJ8P4/gxjoPj+IgFfTgGIoEIhXIv0/kSsYGDBHMpSslpMhPjlOamcPJp3HKWk1eTFMouuaJXgtPOXCXaEycYiROM9RJODBCIxPEHw7huGbdUIDeTIlsu8qXPX4PRBMVicY2fpCedTpNOp7l8+XLT5RzHIRqNEovFiEajlffdxXEcEokEvb29xGKxrjiz1c5qP+0sR1y1EdV41qPd9RbvSO7oCZHOl8mVyhRKLgHf/IDeYtnFMV73iWyxrMluZdNrVylob+zJnVycumnBNuFgr58oudq8VXNzc+RLXrUxf+V5SvkcpXyOzNQY4FWerSVVsQR+/LjWMtQTWlFPjOGeEL2RAH/40GlGp705dgDyxRSxkJ8dPSFioaW7O23XMSUrtV4VDtu1nd/oCnHrEZ8Uk1rX+b21bSiXyy2YwX0j7Io5fNttA/zTE1eYTmcZTaWX6PrgMBQL8uqbexkKeaVomzlY2z6UyM/kODszf9/E6BS58cvkS2X8s0tXTao3lSkSCgZ4yW27mSm5nE3lyJbAOj4sDsG+nQQSOyjnZgmXMpTmJsgnZyllpinkMxRnXSLj4DcWC/j8AXyhMIFwjHBigGCkh5QNEA6FsMUeisVIS++fz+cjHA7XLgDJZJJUKrVsAlvlui6pVIpUKtV0OWMMfr+f3t5eduzYwfDwMIlEoqX2rkW7q0m1Y0xe1UaWCm5nuxtZuCMJu3rDXJ31pgLIFl2cSuBzXYtjDOGAjxsGo5rsVja9dpeCbjT2ZN++fYDXJTv1+Sd5+OrjpJIzGKeAdRduu91yiVxyilzSi89TmSKxngTjFw0vfdGt/PK33cw/HB9bsidGXzTAldkcT12dI50v4VR2bl3r7WQWyi7pQomRvgh90cB17dyuY0pWYj0rHK51O9+p8vXrFZ8Uk1qjJGobefbePnojAR45Pcm5iTTZYhnXWkJ+h75IcEHXh7XaPxAjEvAxm/UCjdOkG5trLdlCid6wn9sG/SSCcLknz7mxGS5PJvna+QlS2RwhUyYScMAY/NYlUs6RK5colcsUinlKuTIRn8XnQNkYyKbIOVOkJ6/gD0VJu0GGdw4TKsTJZMLEYjGCwSChUKh2CYfDS/4fCFwf9MBLjJLJJMlkkrm5OUqlEtZaXNetXRcKBdLpNPl8fkXvnbWWYrHIxMQEExMTPPnkk0QiEXbt2sWuXbsYHBxct26B61GlqB1j8mDjSwW3q93NLN6RTGfyREM+TAFyJZdS2fVKPPsd9vZH+aabhzTZrWwZG1kKOhAI8E3PPsRHn0xxejzF8FAUN5+mUCmvXkgncUul2vK1uFTMEEiP8+UvJwF4RTzOi++IcC0fxRdJMNiXwO84fODh01xL5mrbzWLZ5VTRm5Q0HHAqByvLXJrJEvA5C85IrXb7sR2sd4XDtWznO1m+fr3ik2JSa5REdcCePXvo7e2t7QhXu8pV/252vfjvlfwPC7v7/ShwcTLNk1eSZIplogEft+3qYe9AtNatsL57YTUhqL/UJwnlchnXdRdcbnZdvjIV4Aunxylg6Y/6sW4Z67qUiwVK+ax3yWWYSc5RzucIuiEuPjNXa2cv8PjYOLl0ipBjiAbrvq7GIRSJErF+TDhOuVyEYh63XCBAAVsuUi4VwFqMccik0riOwXXmOPe0S3pqByMjI+zZs4e+vj527dpFIpGoJU+OMz+5XDOO49DX10dfX9+yy5ZKpVrXv2w2W+seWSwWmZ2dZXZ2lnw+v+T4q2w2y9mzZzl79iyhUIg9e/YwMjJCf3//itq5UutRpagdY/I6USq4XWMJl7PUjmRP2E+5cqRvT1+Elx8a4tvv3L3t+pvL1reRE2bW/6anMkWGE163vbhXXZ1iLkMhk6SQnmNsfJyw3+HAUIwdPfO9KbyeBCn6AXJXMekgf3N8mjNXi0R7EuyID2Ecg8/x1caUlMqWoH/hBKWx0PxrW8v2Y6tb78quq93Oz2aLHS1fv57xSTFp5ZREdUB9l7BOuTUa5dZ9O2qB6xsTBc4ly20LXK7r8kanh4t/9xhnr05isiVivhK2mKNcGYvkBMPkXR82EWWkJ8j3PG+Ekb4Q5XKZcrnM2Eya9NkSNhagPxoAt4SlmiQajOPgjztMZl1K+MDxEQoG2d0XhmKOQmaO7Owkc8kkxWyGmFNm/2CcmZkZZmZmOH36NLFYjEQiQSQSIR6Pk0gkSCQSDAwMEIlECAaDS17C4TADAwMtjV+qdtHr7W2+IXVdl3w+z8TEBOPj44yNjS0Yv5XP52sJVSwWY9++fezbt2/N36n1rFK01vEPG1EifT3avVLdMPO6bF/FYpHPfvazxONxYrFY7XojC+LUd8er/g6+fG6q7b+DZr/pQDiKE4yQ9vfhC+3m9r4gb37ZboZDJaamppidncV1F3afujQ5x4lTF5hO5gj3hrky/gyBSJxgNEFPIMqc9VHAR6Hk4vcZckWXdL5Evuj9347tx1a1UZXzVjuFS6fL169nfFJMWpmuSaKMMXuBdwOvAQaBK8DHgHdZa6dbWM8A8F+B1wG7gUngX4D/aq0dbW+rV65YLHL+/PkFlduWKtqw+Hrx34tv8/l8C85krVS7+vFaa8lkMszNzTE3N1fr1pZOp3FdlxcnZpg6P8Z0usBEyV1yHNbORJjX3rmbQ7v7MMbUutBN+lIEd/kZ3mnY3d+DLxDEOI7Xj926lYIRReKpNNemUmTzBQqlIlN5iDhBioEEmViIULiP3nKG23otcae4oEx5Mpnk6tWr+Hy+2s5DOBzG5/PVkqr+/n4ikUjt/fb7/fj9fnw+H0NDQ7WzQis9e7Ucx3GIRCK15Mh1Xaamprhy5QpXrlxZ0C0wnU7z1FNP8fTTTzM8PMwNN9zA8PDwqnZ61rNK0VrGPywXSHPFcm2QrTGGZLbQthK07R63sZxOzrwu19vqcakqlUrVzpIv5jhO7cBfJBJZMDa0elsrZ++b2YjxJa3+pu+u64blui4zMzNMTU3VLhempshWuuw5xmBdSyE9RyHt9aoI5ktkCg6FYAxCcZxwnCwRLk5nKLu2rduPrWajKue1awqXpcRDfk6Pp3noqTFu2hHnlbfsaNs2fiPik2JSc12RRBljbgIeAYaBvweeAl4I/CzwGmPMy6y1kytYz2BlPbcAnwb+GrgNeBPwHcaYl1hrz6zPq2gumUzyZ3/2Z/h8vtrOeLNLdSd9qf+XClbGmNp91fU3+vvY+Rke+OolJtJFckVLNBwg4PNRxnCpaDl/MchXnjzNj778Jl55667a413XrQXadDpNKpVibm6u1p2vXC5TKpUolUq1v3cGyrxqf5Bj55KMTqXIpYoUrcXnD9AXDrG/L8GLD/Vz42CIUqmE3+8nm82SzWa5em2CzNQ1LJZkrvHH7wMGnTJjpTwl1+K4fozfC/z9PTEODO3jpTcNsrcvzOzsLOPj41y7do3p6ekFryeZTOK6bi2Ri0ajtbFQ4XCYnp4eYrEYgUBgQUIVDAZJJBLcfvvtHDx4kGg0Wku66t/31R7NdRyHoaEhhoaGOHz4MJOTk4yOjnLlyhVKlT781lquXbvGtWvXCIfD7N+/n/379xOJrLyAxnpXo1vt+IdGgTSdLzE+lyeV99rtza5uKFvLqbEU//zEFd78yptW/Prb3W7Z3LZDXBod9fK38fFxksnkdXHHGIPrumQyGTKZTNN1VceQNku4Go0thY0d87ja37TjOAwMDDAwMAB4293J+EmiVx/Hn07iN161v3rxkJ+AU2Y2O00uNUnedb1eE719jOwc5M79e/iBb7qR5x9q75iZrWC9Y1K9dk/hUh+fCmWXSzNZ7n/wFB/76qW2FpxQfOqsrkiigPvxAtVbrLXvq95ojPld4OeB9wA/sYL1/DpeoPpda+1b69bzFuC9led5TRvbvWLV7ljVrmprKa9d3SkPBAILzoxUA2AgEFhwW/VijOH8ZJq/evQC15I5okEfA+EATrbSlQEIWktyqsCJk2n+29c+y+dvTBBziqRSKbLZ7IKEqVwuLyi/vvisWTAYJBKJEIlEeOm+KLm9caZzYP0BoqEg+wbj7OyNLkjy6oX8Do5jyJeWrz4TCviIhrxqNc/b18dIf5SQ32H/wML+7NUAeOutt5JOp5mcnGRycpJ8Pk8mkyGbzdbGLFXHf1VLkltraxULq0lrtThFMplkdHSUQCBQGyOVSCTw+eYHDy/+POp3Vuo/06V2ROrHxlUTqjvvvJOrV69y4cIFJiYmas+Ty+U4efIkJ0+eXHB2arkjxRtRjW41XQSWCqTTmQKXp72KQdWj1QZwsZTKLinX8tdfvsjNu3raMqBXXRu2pS0fl06cOEGhUKBcLtcOYFUnFc/n8xSLRVzXbXrAr34+vPptWfW2+oNI1Sqn1bNX1aI9Z6dyvO+hs1ycLdAbDXOgzztYZRzvcesxvqQdv2ljDIN9CWKDu3ASO9jZF6FcKlKoFKooZOYoZucIBXzs8DsUiiXG5zIEfIYju3zcfWuIHT0prjz5Zf71dJD+/v5a/Ojr6yMYDDZ87moBomqCm06naz1CisUifr+/ti9Qv0/Q7Lbq3+3qVbFWGxGT6q30O7Fccrc4PgFgIFcqc3o81faCE4pPndPxJKpytO/VwDng/YvufgfwZuANxpi3Wmuv72swv5448AYgDbxz0d3/E/jPwLcZY27sxFE/Ywy7du26rmjD4oINi4s3AAuu64s65PP52t/NWGtrxQo+98wE5yfT3gSAQT/TgFsuUi4WvEupgFsqkiuWmbPwrxeXr+bi8/lqAbF+3FB9ApHLeUfn+g1QBjIwnhlj/OL16woEAgQCAWwRmJtgLlsmZnvwB/wYnx/H58fnD2B8AZxAAMfnB+PDpcDewR7e9O23s7tv/gxMNQGpf8+r72n1vZmdnWVsbIxr165RLBbJ5/OkUqkF3ROr46aqSU2xWKx1YayekQoEAkxPT9deQ29vL319fQwNDQHUzhy1whhDKBQiGo3WdkCql3g8zpEjRygWi1y4cIGLFy8u6O43NjbG2NgY4XCYffv2sX///tocVYttRDW6qla6CCwOpOl8icvTOXKlMj5jiAScBWf5rPXK3I+n8iva4Wol8LSza4MCXvfaLnHpq1/9KqVS6bru4o7j1LY5iwsIVYvf1B9MK5fLSx4Iq1efXC0+ePSvJ8Y4OTaH3+8jEAmRMQZjvEqsjs+H4/e284UCfOOsn9+eepo3331rbZtbf71cL4/qpWq1v+lyuUwul2Mk6hIszHFpbIZ4Pohxy5RLRdyyd6DU5w95xSoyKQrZFLmpWcJhH3usj+w0TJd6iEQiFAoFZmZmFvTm8Pv9tW19tZt7qVSiUChQKBRa/8BXqJoAV9/T6vyG9WPlmp1VbJeNjEn1lvtONEvulopPhbJXkGEo7s0j1soBgU7Fp9U8/3bU8SQKOFq5/oS1dsE30lo7Z4z5PF4wezHwqSbreTEQqaxnrv4Oa61rjPlXvMB3FNjwYLVz507uu+++2gZy8aVYLF73d7FYvO5SLaENLFgmn8/XNqyFQqF2FLFQKNSOJs5mClwamyKbyhDzl0mWirilIq5bBqw3BbWtdIuyllzBZbyU5VrIJRr01wJrfbeN6pmY+uBUf4asel1/pHKpMzD1Z7HqnczG+NrFafI+Qzg0f1TOLZehXKZc8JKz2WwBp2wZ3mEppyYZKwRrSV39dTUQVbuWLO5iZ61lYmKi1l2u2i1xenqa6elpUqkU5XK5VpAiHo9TLpcZHR3l6tWr5HK5WqnzqkAgQDwe58CBA+zbt69hEtOItZZcLldLRJcSjUYZGhritttuw1rLlStXGB8fr92fy+V45plneOaZZ9ixYwf79+9n165dC97vjapG16rFgXR8Lk+h7OIzhqB/4ffF4s3LEgn46IsEmg7o7dT8Hp16XmnJlo9L1loSiQSFQmFBF+z6xGglqmeYqj0s6g/61R/4q96+OJFKFcqcG50gky3RHwuQyTfeUbbWMp0u8sXLARJTT9MbuX5HfvF44qXGG1fn4lvcM6CaOFTPnFWTxVKpVEsaC4VC7fXUzrZdvErhWppzo4ZEJODNQFqZyd66Jaxbxi2XyRYhHI2xuz+AKWZ46qmx2pmk+oQpEoksmaQ4jkNPT0+tQFEr3bVbUZ2aozo9x/T09cP/gsFgLaGqJnn1183Ooq3UZolJ9cnd4vhkgbLrEgw4xIL+FRec6HSc6PTzbxbdkETdWrk+2eD+Z/CC1S00D1YrWQ+V9SzLGHOswV23reTxix34xY83vO/Xv/dO/sOL9gPwf750gV964BvYUhFbLmJLBe9SLmJLRd76qoPMJuewxRx//9gFrsxksG4ZXNerXue6YMs8d6SHb7l1kHw+z+hEkn88dsZbp+sFxuz8K6UvEsHn83ZG0/kiuZKLcfyYnhDZYJivZqM45RAjQ/387Ktvr536/9m/+ZqXhJgyxlcp1e4Da+DNr7yBb7tzBL/fz789OcYfPnwOjIOBSnvLWOuCW+bDP/aCWoL4rn84zvmppfvez2aK7B2I4hhDrlhecrlPHL/MJ45f5le/41kcGPLmu/qzR87y8DMT1y0LhoM7e/mdH3hhbRzTt/7BFzH+ICYQxvgDlFPTlOcmKGeT/PCLb+CVd91CPp/nE189y0f+9RhuPo0TCOGEezChHnAj2Hyatx3dVxtD8NePXuDa3DgcG8M4xzCBML5YH/7ETr73Fc/mHa97HuVymROj0/zon35h4ede+eyBFb4mGOmL8IOvuB1fOEYAy6999Eu1dSx49Y6ft33fy/ip734J0WiU//OlC3zokXO1+89MLHx/X3zjQK1bzfmpLGXX8sHPn+ODnz+3YLkfeOE+/vv3PRuAJ0Zn+a7/+bkl2wnwjz/9Tdy519sQ/9Lffp2/evTikssNxoLEQn6uzmZJ5cu1Lp65Jbp6+h1DLORnV2+YE5eTS7axam9/pDb+4tpsjkLZO0DxlfPTC96L9XhNPgN7+iO1cR8nr6Wue96qxduIX/67Jxo+/7nf+I7a39/5vs9y/FJyyeXW+prqn2cL6rq41O6YdPCX/onMqWt4hx4W+uEXH+AVtwzhui4PPnmVD3/xLFgXXNfbbtf9/QMv2EuuUMRvLA89PcZYMlvbtmNt5W+XO/YkuPuWIcrlMlemU/zdsTPeeqzrxS1cUjPezuhALEigcrZorlAmW8KLH44DxiGTL/ChR86ysyfM979wf63df/CpZ657LVXffNswh0d6cV2X46OzPHhyDMolbLmEdUve35WE57XP2lFLIr9waoJk/voeBBbL3r4Iz9qdYC5bYPZykrl8GYzBYLAY72+fg8FHNBTAdRx6Y1GupOF9xzPzb711seUytlwiEXQ5sjdBqVQiGAzyqaenwOcHx+f1unAcMD6M4/DNd97Aq19wOzfccAPHZwP8z89dxgQjGH/A+3zckhdr3TKf+4VX1g7UvvnPvsipq8nKa6/uO3hx+RWH+vnhlxwA4NxEml/7+DcavqdNY5JxvFjqC3JgZx8/+spbsL4QwzsG+fm/fbrhOhdv67o9JlWTuy+embpumWp8qsakUMDhWjLH2brXsbi9537jO2rjAx+/OEOxfP3v8yvnp/n41y/zm69/DkdvHW7bazo8kuD//czLa8//5XNL1875yvlpHnx6jHd+9x0cvXV4W8ekbkiiqqnsbIP7q7f3bdB61o11y5XkqG4HuVTkzJOWz5cuksvl+OrXLpA9e7bhOj78cNabKdoxzBQdnMpcE9ZabCkPhRy2WKZULJDNZvH7/fQkEvh6dmBqR0ucykEy7/9EX5RoLIYvEGKq4JAs4G0AFx0QjPRH2bVrF+AdqXICQbDWO/RfLuHaIhRcsJaJq5c5HfUq4F08NU7h6ihexDAYnw/jBMDxgc9PoVDA5/PR09ODP9KDE/F5FfjcciXAekmia+HyVJqQ3zsT5ebz88lYJVBjvMA1PT1Jr89bbzGfxRaLXvBx6rt+WdxioXaWCaAwPv/eG8ePE47jRHrw9Qyx+4YbGBgIMDU1RaJ/kMDAHu+sYDXpyc+BL4AT6+Ouu+4im80yNTWF7/FrGPJe26zFFrKUCllK01d48nOX+WR8kltvvRVjovh6hnACoQXvu7UutljgOUeex40DQbLZLL3P5PFdKmFLedxi3nv9FROpPB9++Mna9wTHhxMIe8lZcf5slnVLXBs9z6c+lWHnzp3MTvmx1jYsgHFpJlur9hPyO6savLtafdEAA7EgT12dI19q/rw+x7Cjxyv1ulw7gz5Tm99jKlWgsMKj7+0QCvgY6YvUqgtKV9kWcSl8w3O8bbitT47KHLztVp73nF2Uy2XOlC4QOmPqEiN3/kCYW+ZLU0FKJT/GlslEdhAIXH9gw1rLrpsGOHJkP+VymbPjc/zTlUjd89r5RMJ1iSf8BA2UyyWyySxONot1Xe9gULno7fTbMlk3wMWLpnaWq5S8VnlG43WuMHjxyS1x8fQ0hWtevLk4MUd+fNa7j0r3eov3N5YzZ+ZqPT7SU3MUC5XeGnU9NgCms0HO5qJeMaJ8kelkvnYWylRikfe3QzbgJxL00xuIMpUqUErmKk/pxS3j+MAXxAaj9Pf3187kmLMpbNmFUh63lAfHh3H8WJ+P5PQk3/jGNzhz5gxnZkpkLxZxYn34Yv044XglifFj/EF27NhRqzyb2DNO0C69I3vo+ft47WvvoFQq8fj5SUKPW2wxhy3kcYtZbDF3XcxZknW9xxVzXLqU4g//YbwWk7LJIk6kByeSwBeOY0KxVRVe6oaYVC0v3kw1Jq1E/ZyIzd6RZK5U6w7YTvXP38xkXXf57cwsN55m3RtgzP8Cfhz4cWvtHy9x/3uAXwZ+2Vr735us55fxBvq+x1r7K0vc/+PA/wL+l7X2P62hvcfuuuuuu44da3RQcGmTk5M88sgjLT/f10dn+KcnrjCdLpCrKxFeKBbIZnIEjEsiCC890Mtte/pqY3HqB49WuwV85ewE//D4Jcq+EDsH+/CHwviCEXx+f627QfXadctcm54j4Bb5rjuGeNbOSK1LWT6fr3UXXNzFsDrGaHGXxPrKffWX+q4j9V0mqoUqqtL5EhOpApl8mTIWF3CMwWcM8ZCfHT0hokFfLQmo/15X11PtylE/7iocDhOPx2vjlvbt20dPT0/Dz6Naoa9UKnFxbJYnR8fIFctLFrHo6+tjYGCAYDDI+fPnOXHiRK3PO8Bstsj4XI5i2RIK+LjlhhFuO7iX3bt31y7xePPyqeAltP/46DP84Sce5/KVa6SSM0uWku+PBfnuu/bzokO7al0+F4tGoxw4cICkk+Afjo8tqPbjGO9Ufqeq/Tz41Bjv/n8nuDiVxbWWgM+p5qW1rgZBv8NIX4S+qPedH0vmcBzDfXcf4vsqc2S88x9O8MknrxH0mabdQ8aS3pmpb33WTt7xXXesuf2Ln7dRdUHvtRmO3jbM+37grjU/b7c5cuQIjz322GPW2iOdbksjmykurTYmrVZ9Fb1UJk8saPDhUiyVmEtnCTtlhiIO9zxnmGfvjl0XN+ovuVyO4xcm+MxTVyiWSsRDyx/XncsW8Dvw/P193DAQqcWM6nNks9na9i2fz9fiyuKYsHiy+2rcqK80C1zXJbF+LFh9Vz/XdckXS6TzRYrFMq51K7mWxcHgcyAUcPDVdTesdnWvj3XVMdHVWFWN46FQiL6+Pvr7+2tdDYEFlRSL1iFTNhh/iL7eXm45MMLBPTvo6emp7Qf4/f5ad/zquLfq/Iix2Hwy02wsTLWLeTqdJpPJkMvlalV1q38/dm5iyX2XxTHptXfu5q4DQ7WCSTt37lyyy/vxS7PXVaDrhphUX1HSGJjLlao59pIxqV43xaeq6vNba71xZovik88xxCtFvIwxbX/+TlhLTOqGM1HVI3GNvv3V22c2aD3rIhRaebZe3chdmivx4MUSU/QQSbgMOkVsIU8pnyFEkVjUIZUtMjlX5POny8RCPobjwQUb+Or4n2g0yq6eINFwiGtzeXKpWUxqhnIhT7mYp1zIUSrmKRcLuIU8pWKeqdkUMZ/lsbEIXy0Xa8GwWrmp+v/iILnRJvBGf7dLb28v+/fv54YbbmD//v0cOHCAG264gUgkwtzcHMfPXeXzpyY5P5kmV4Zy2cWWC4Qdl4O7Bnj5LcPcMBirTeoLEI/H+dZv/Vbi8TiPn7nKX3/qy5w4d5LU3CxuuYxxDMfOTTH82DM8/+bdPPvmG+jv7yeRSLB7925KoQSjabNkQPvGlTn+8vFJJpwBhm4a5uawQzE9Q3Z2gvzcNOWyy1yuyLVkjn947AIxP9y+bwf79u0jl8stGDuVyWT4xje+geM4fPfePdx750EupuiKQaVHbxvmmWtz3P+Z02TyZRxjAG9nIxhwiFWS6VhoftD44mpNGzV542KLn7dZdcGyaymWXR58aoyPfOUir3/+vlU/r6zalo9L1lpSqVTDMblL/f/MlRk++LlTXJlOE/FDTzjgzY2EtzPRay1zuSKnCmU+dO0CP/DC/dww6HX3qk4dUZ0UvJqAhHt3cHw2yNXpOcIRg3VLXhwqFXGLBcrlIm6phFsqUirkSaUyRJwyE4wzc9EuOJhXP7armtw0Gu9Vf9Cu+v96H1TOLr/IsowxxONxent7icfjBAIBCiXLXNlQIIjr+DHGwRcI8tljxxnsjXPr3gEO7d3Fzp076e/vp6enh1Qqdd26HcdhPGf44sUs5+ZccgQxwSiBYPC6sTATWcuJq0UyBYdosIc79u2tbSOPX5rlj898jcxgLwPDZXqDXo+NQjZFIZ2kVCzWYtI/PXGF3kiAUqnE1atXOX78OIlEgl27drFr167a5PTdWIFucXnxSzNZUpSweONyl4pJ9bolPlVVn38m4xUtKZbtdfGpWHQpll0CPi/Zbufzb0bdkERVO8c26hN+c+W6UZ/ydq9nXYTD4QWTFlYvU1NT11U2yufzTE5O8jcPHufU2ctQzGGCvvkTxtatBBWvol4+m+NiscA/jgZ41u5E7ahYoVCoHR2qDl69Np0km81yrpBfcpzMYpPAhXV7V7rT7OwsTzzxBE88sbCP786dOxnYOcJccAAb34HpGaanfxC/z6GEw3imxPTJC5w8fY6X3jTAcw4M18qjp1IpUqkUXx+d4V+enGDOxCjtfz69Ppfi5CWysxMk5+ZIT6UZ/+pZxqdmuXl3H4VAjJOzPkZnC+Tx4Y8NEOkbZHBgkDv39nHvkaVnTg8Eh4n2D+OWS+SS08RmJ7y5sdIFHjk9yQ2DMTKZDL29vdx5551kMhkuXLhQK73vuq43h8zoKIODgzz3xhvZuXPnque6apdvv3M3n/jGNU5em2M4EcJa72hfLOj1N6+3VLWmjZq8cbH6580Vy8tWF8wWXbJFlw88fJrbdyc0gHfjbYu49NBDD7W0/Ce+doGJmRQRv6E3Ml84wLreQTvrlohSpljMce3aLP/ypRSvuWP4umRsceGKUOoapclprrlFQo5LuViknM9SKuQoF7KUCnnKhTzFQg63WKJgXB47X6qdcep0j5qNZK2tlTK/juPHH4njD0cx/iAEIqTmehifmWV8YorhntMEAgEikQiDg4Ps3LmTHTu8M1XGGB6/MLXk2SPrC3LeiXD2TILPPnGa/buGmCvQsODAA8dGuZYq0d/bc93ZFGstpXyGgXSSq9fGmU3N1mJSVTKZJJlMcvLkSSKRCLt372bPnj309/d33eSv9cndZ06O8SefO8tYMs+NQzEiDZIn6K74VHXi8ixjc94ZLmttw/hULFvyJW9OzbG5XNuefzPqhiTqwcr1q40xTn0lJGNMD/AyIAN8cZn1fBHvQM/LjDE99ZWQjDEO3iDg+ufbUH6/n+/4Dm/Qm7W2Nl7mK1/5CmfPnmViYoKpqSlmZma8stnpDGevzZDP5fHZkld6vFjALeZxy0uXyZ4EntzA17TdVCezref4g0QHdxEb2ElkYDfl+BAz8SG+OuVj11CRWOoas7OzBAIBZguGTz0zw1QB4rEovbEofp+PwNAgzs5hCrksk1PTpOaSfH08TzI/wcmrZ0jly7iBKLG+QcL5HHMTl7joOpzq7eczj/ZQ8oWZc8PcMjJwXZsdn59o/w6i/TvoGTnEkydPc2F6mvG5PDt6QrWEcceOHbzgBS8gk8lw7ty52hk0oDaXVjQa5eDBg+zfvx+/vzObjvpqTa5rW67WtJGTN9arf95m1QXBO9Ls91XnxiksqN7UTUdht7gtH5eqFeoWT7tQPXuz+EzU2EyaJ0+eZzaZYTDqY3Z6viAEVIo+AG6pgJsvMDuX4sQ1l/BUDOPOz2dUndOoemApnU43rTy6lPUr7r3JuSVK6RlK6ZnaTVlgxvExFgjSl+ihv8+r7NfT01Or9Ldjxw5KwR4+eyHLdNFHLBxkOOLD2HJtvHXYnWVy/BwXckWewBIMBojHYwSDEWwwzHkb5PTJEA99wU++ZJnJW27cPUA+XcAfDOH4g/OVEUNRfIEQe2N9nL6WZDbmIzywm0AxxeTkZG3Se4BCocDc3BynT58mFosxMjLCnj17SCQSnXmPG9g3EOWHXnyAU2NpPvnkNebyxaZJVDfFp6pMocxstojrWvxO4/gU9BsKJZeSa5lOF/jimcltG5M6nkRZa08bYz6BF0x+Cnhf3d3vAmLAB+rn4jDG3FZ57FN160kZY/4Cr1zsO4G31q3np4EDwL92amb4n/zJn+Qzn/kMk5OTTE9Pr2my3W4XCASIRqO1ineLy59W+2LXX6olyKtjlS7O5PnyhSRzBZei6xAKOjgYXCy5QpmgscR8Ls/eHWE4Qq0vdrWbYTX41/dlr+/DDiy4r1AoMDU1xZUrV2qDlFfCLRVIXbtA6trC83UXQzFO7dzNCw7fwt69e+nv7+crF9OMjyfx4eLm/MxOOfiDYXyBEL6Qd90XCYLtYS4b4ETGTzkSJhxIEyjnYO4KNhci1tNPXyxBKj3FmYkxyq4lFvQzlRsknBgk3DtAIBy7rq2BQIDBkRsIOfspxwP4fKna0eDx8XHGx8fZuXMnz372s7HWcubMGS5fvlw7ypvJZDhx4gRPP/00+/fv5+DBgy2Xa2+He4/s5cTl2dqA3sF4aMFRu2q1pmSuxMGhGPdU+prDxk/euPh553KlWh/zSKDxnDrWgs94geqJ0Vk++Y1rfO7URMfKzW635G07xCXXdbly5UrtTE79ds/n89UO9lWTnm9cuMbYuasUchnG3QLFfIZyLksxl6GYT1PKZXCLC9Obdne13gjVWFQt010dX1xwHabzZcrWweKNVzKO97eLwe/zEQ4GOLCjh+He6ILxTotLrS/uRlg/DquaQFTHRVU/l9nZWc6dO8fk5GTrL8otU8pnmRjPMjE+dt3d4XAYJxTDDUQI9vRjd+6jmOjH5/NTrTBVKrnMZYqUyi4Y8Ad9OMUg1RTcsZaZElyxfvAFicXjzBT6vYqCgHEMjs9fGXs9/10rpfNc9vv4fHyKIzcMeGXvUymmp6eZmZlZcMayfkqU3t5eDh06xG233ca+ffs6dmBvsc0Yn6oyhRL5klsbc9yMY6DkWlKFMv/8xFU++8xEx0qgdzI+dce3Du4DHgH+wBjzKrwTKi/CmzvjJPD2RctXT7gsTtd/Gbgb+M/GmOcCjwK3A98DjOEFw444ffo0Tz7ZfeeJ/OEovmAUXyiCPxwlFInR35fg0J4d7B3urx2tisVitev6Sfeq19XJ+BrNbwHz81pV565afKnOV/KN0Uk+/alvkA6HiIQK9JsStjIJcLlcJGiKZPMlJksuXy3lecUtOxgevr4YhLW26bxb1WTKGEM0GqWvr49YLEY2m+Xy5ctcuHCBsbExJiYmlpwno5lyPs34hVP804VTC243gRDR/mHC8V6C0R4CkTiheC/+SIzq19n4AiTTXgWseNAh3hsnGBsBoJhNkZ2dIDM9RqinjwAxCq4hXyp7kzlmUiSvnscfDBPuHSDSO0QwNn/ELuAYcBwG997INz9riKeffpqLFy/WEqXq2bZdu3Zx66238qxnPYtz585x7ty5WuJfKpU4c+YMZ8+eZefOnRw4cIAdO3a09P6sxeGRXu67+1BtQO+psRTxsJ+AYyhWukjEQn4ODsW47+ihBRvyjZy8sX7DnimUCPl9XMp6RTGqg3KXYivPHQn4iIf9XJ7N8hv/8iS5/5+9/46SJE3v89DnC5MR6bN8V1d7N35mx6wBdrEGS1AACJAgsSSAC0qkeCUaSDoSyaOjyyNKICTy3iOeK5ESeMkLOlEQcYklzIKAuFgCu1zsLtZgBrNmZnrbTPsub7LSh4/v/hEZ0VnedFZVVk885+Qpl5kVGZEZv3i/931/rxcmduyxLfpS02G+EZVT/MwnLvGJp8b3vJ3b8R6fFfJE69I3vvENPv3pT9NsNjdkiTqdzoEOc+03iqIkOhQv3MX9wL0Leb0Dy9f/LQ6aNE1Lgh6I9GG6ZvFr35xBaTrkMhoFU0dVlCTACGRIy/KxvBBRNPj4a6c4PZRNTCl6e7F6Sw9jE4ne7+OfwzBco42xaUa1WmVpaYnpuXmu353BsW1E4OJ1WkRZwb1j2zZ0s4H28jSNu2+j6hn0fJlseZT88ARedoRAL6CoEPoOni8JgjAZjyKEIKuD07awOi3CTg3DWkLVdPRsAc3IoWdzKOra6wOtm02Jx1aoqsrw8DDDw8OEYUiz2aRarSatD3FQGQ+S/9rXvkYul+Ps2bNcuXKFM2fOMDIysmag8mFyHPRp66Cj5/9tU7rvBSFu93hJCW4QJmXoB61JvQyCPg1EENVd9XsN+B+AHwR+GJgD/lfg56SUu7qClVKuCCG+h2ii/I8B30dU5fa/A/+9lHL6ADZ/VwwPbyy1ehyiE1IePVtAN3MEeg4jV+Dli5M8d24ySdeXy2VKpdKa73udeGZq9p4i+M2CEdd16XQ61Ov1Nb9bf7/1deuu6yZGFb0GFb/79jR3l1rRCcTQNpiHCiBnaIBPy/a5PtdkvLixrEsIscadcD1xVioeKthqRaUE8bY+/fTTvPDCC1iWxa2ZJa49XMKzOuB38NoNnFZjV31lvUjPob34kPbi2jkNiqpjFMtk8mX0fBlHLSByZZRKmU59hcb8A8LAw8iVMIoVtGwRVVHQrBa+p4I0u82ekaD5rk1raZbW0ixqxiBbHiVbGcULtWTlyjRNXnrpJS5evMjNmzeZmZlJtmd+fp75+XkmJye5cuUKly9fZnp6mjt37iQNyVLK5H7FYpGnn346scA/aNY39MZuTVlVYaJobunWdBjDG7c6sdc6Hn4YNepq24ijF4RRn5ehoQhYabs0LIXxYiaxY4+JVzXvLrcTu9l+icZ656nDDN4GgSddlz7zmc/wO7/zO0fxrzcldoorFApkzCxCM1D1DKZpMlTMUcpnk6xQHPjEgVIc+OyVMAxpt9u021FCMS5xjP9P7I73pe8uslztkNFU8poaWayjRuM6hIKuZhguGdQtj0ag8O15h/ddOrVh6HycnfJ9P9G9drudZPxarVYSwG6mmZlMhhMnTjA6OopaPsGCfhLXsTCFT+j7eHYHp13Ds9oETpQljAfS75XAcwlqS9i1JVbvx+sDCoqZRykOkymNYY6fpDJ+ErNYjgeYIHMeXstByhDVVBBhgNtuYDejj4uiauhmDj1bIJMrEKoGZkajUooWaWFtQBn3b7muS71ep1qtsrq6uiZz2ul0uHbtGteuXcMwDMrlMqdPn+bixYucOnWKSqVyqP28g6pPOwUdw/kMhqbiB5G5UWaTbFQQSlw/JB5fpYhorlvsTHyQmtTLoOjTQARRAFLKh8B/vMv7bvlpkFJWgf+yexsYxsfXHkRd1xML7JGREYaGhtbcyOT4t9frrPg6Z06MYuSLSeZCN3NJihyiN+2txRYXxwr8vZ94344fqHiFq9PpkMfl5TEFzwtwXYvOUoN3ZjcPglzX3XMDbxAEyQraene/3jR9TN2KHHu8QFIwH71GQTRHQyhR7b2qZzDLBss2OMUiF559ljPjlTVBU3wSjlcDe39ut9vcejjP9XuzrLYcVEVnrFRkIqsn2x0HVwC50hB63kPNFjCSE4uMmp3tFp4VlbN4dge33dh5hsb6YxJ4WLVlrNraAbotVcMsDmOUhjAKFTyrTae2GJV7aTpqtogkh2cWaRc0RoYqyCCyqU+OgevQWpqhsTjNQgcunD3NudIzyd8LhQKvvPIKly9f5saNG8zNzSV/m5ubY25ujhMnTnDlyhU+/vGPs7S0xJ07d9a4+jWbTd544w3GxsZ47rnntrWJ7xf7dWt6nHKLndjuxG55URmfH0bvQ11T1qQsJFEAFYQSU1cZKxrcXW7hB5KyqW4qqKoS2+DazNXtNf1Tj0PvrJCSqR1q8DZIPMm6dFDZY0XLoGYMpJrBMExOjZUpFaIqhbiSobeqITZd6s0y9IveoGj96I/Nvm6WvVhqOqwKG5nNMFI2u66gG5FSUjDUKPswt8q9+SoVU0nMnXotwLcq54/L1eIRH2EYUuu4LNY7uH5IRlMZL2cpZ3WEbqCZeVQzTy6jEPgevmORLQ8TeB4IgaKqKHomyhLY0eD200WVjN9O+rD3Vh4YEtpNQruJv3Sfzm2YBoSqkRuaoDA2RXbsFBhjkBtC5jSKuSyK2p39KMNkYLIQ3X4ey2K0NMKrT5/j6XMnGR4eThwc1+/fuOTRcRzu3LnDjRs3uHfvXhIEQ2TMFWep3nzzTTKZDENDQ5w9e5aLFy9y5syZXY0OeVwGTZ92E3QYmoKhKVhe1JPrEi3M9r7j3SCa2QlRskpXlTXbd1Ca1Msg6dPABFFPOn/9r/91fuInfoKTJ09y4sQJstnsjo8RXb9+VRUMb7EiEQY+i6tNjMDjfN5AdKrcqc0nAUAc/PQGResbiftBEARML9e4M1el1bGQnks5I8mqa2dzxCtycdlf70qdqqpcW2ijDStURjOMj1TQzDwZMxeVHeoGWsZE0TMoSiR2+ZpFVleZfOEKn3x+clfbmqzGiBEak1dwhx28xhKzTp2TSF4c0xk2ZBL0ua5LXVbJ1gWe52IIP+oFcNrIMETPlTCKI49WumRI27IQYcBkXkG4beaXVlheXoItTEG2QgY+Vm0Rq7a2jl3PFtCyBYxsEU/PglFkbrGAUymRrYyTLY+gZQsQBtEQR6Bpe2SkZDSs8u53/oDFe2VOnTrF1NQUhmFQLBZ57bXXqNfr3Lx5k/n5+eT/xRmnuMzvQx/6EK1Wi3v37vHw4cPkPbW0tMSXvvQlzp49y1NPPZUMjDxI9urW9DjlFtux04n9RMnk7nKb5ZZLIKPZZ5vNujK7Q3gFMqmNP7HNiiT03+52M8fHXg5DKFMOlvULe5sxNDREpVJJgp4FC2qeTiaXpVgsoZk5dCOHZmRR9QwIlTD0aVouAYLnpob41Gtn1/QEbVfC1vv9+p9776vretKvFPfSxj/33uL5SVJK5modbi00sbwANMHp0TwTJXPN866/hWHIjdUlPNWkUNTJZPVoYLqU3a+RuYYMAwLPwXdsRLvJcsPji19d5NzwzjrfS9yjC7DYtLmx0GG5E+BKJRqargpyHTg/WWLi/BQVpYkbBJS1gNBzEIoWDddVBIHr4DsWvtOJvrZtdFXhqYtjvO/CiSSIBfjc177Fv//6t2gtz+I3lnF7jCl2gwx82ssztJdn4Nrr3d8KaqVRKhOnyA2Nka2MYuTLaGYe3cyhGTmajo+pCk7mJNbKHG+uRAt4+XyekZGR5JbNZpOAOB4B8/LLL/Pyyy/j+z7T09Ncu3aNu3fvUqvVsKxHZvKu6yZl6q+//nrUGzwywvnz57l8+TJTU1MH2ts7CPq026Cj2najSgghUFWBF0gsN+iWn0MYRs58AArR4nZGVchnNoYSB2HBHjNI+pQGUYfE2bNnOXv27Lb3kTKaeRHPW/q+kwqvv1XlwWydliopGgIRBtH8jCAKiJqWS8cNmCiZnD1/hm99a/Mp5P1CCLGmpM91Xe4s1PjDO8vMNRycEEBB0TRyhs6Z0RIfvjzGhfHSmoGCEGXjCoVCcsvn82TfrfFtYxaEsm0qO3mOPbrT/Os3HvKPv3KblZaL64eUszqmrqNUJmnYYwSGhp8x+Y9eGeNSwWd2dpZOp8PJCza3/RvMrzYoZwXS8wh8F89q0qkt47UbBGGAEJHYhUKnkM9x/swQw6U8PirfuFen1uqQxcO3GtHMjGYNq1GNJsDvAc9qRf1RPAp0OpksrXyJbLFCtjJGrjJGdngcozhCx5N0bJuJ4RIfOj8ERI3K9Xqd7373u2t6m8rlMu9///up1+u8++67azJTcTB18uRJnnrqKZ5//nmuXLnCjRs3uH//fnLxce/ePWZmZrhy5Qrnzp3bcOyPmv2WW2zHbk7sl8YLWG60Kilg21lXd5daCMDU1W1dnuLn7pfd7VHPKkk5HF588UX+8//8P2d0dJRyubzGSEFVVXK53JrPbRiG3Jpf5dNfv8NCrQkiQFdlNzAKo3l3uoYtTAIzx0TJ5AdePkOlstHkZit6B8FuZkYUb+NeziePSph8GrZGEEbN+6UleH7K2LFvYiE7TWmpgpSRG2gYBvhWG7d7DvasNr7TQdEyZFSdLBkEMH5mgufPDa3ph+o1kOg1moiJg7+bSxZvLy9Ty5TpEKCp0ZweH6gGEnvJ4WEjQAYS24MT4ycwcgUUVUOGIZ7dxus08Z2olC+UkvZKk2Ej5OnzI4Shx/z8fBKgTo2UOfHUy6xeeJUTwyVCp0Nj/j6txYdYtWU6zVXcZh3CvZSvS/zGEsuNRxULKCqFsVPkRydRCqOI0jjjI0O8MDZOEARJJjAusXzwIDJsyuVyjI6OMjY2xujo6JrFOU3TOHfuHOfOnYsyd7Uac3Nz3L59m/v3728w8vI8L9Gxb3zjG5RKJU6dOsWlS5cYGxtjeHj4UCoptqPf+rT7oAMWmw6qIihndYJQ0nZ8/FBCt2BTEd2hu933ZN7YOF4kfs5+W7DD4OlTGkQdEp7nUavVNp3aHt82K5f78IhFfXaF1ZrL8hZTvydKJj/8wuSaOQvb0dsr1Lt6t34lLw7oeqeQW5aVZI1M0+St6RpfuO+yapvYSgbTeLR9y05Ia9Fiya3xE5UxPv7cmTUB02YDiEdXQNMWo9XC3ezXXbrTvDNT5xe+dJsv3liMmn8FaCJySwtldOF6omQmKeBffBN+9kef45PPPEO9Xmd2dpbff9CheXMWT4SUy49O4sNSIgMf12rSWV2iujiPFrYoZgTSs1la6kQuhL6F8GxcIciVRsiPTKLqBoqeIfRd3HYDz2rTrFex6qv4zWXYQ8+VdC0c18JZXaD24AYIBS1fRs1XKIyfYerp9/GJyyPI5hLv1uYT05BcLpeISqFQ4Ny5c5w6dYpyucxrr72WzOvoDaZmZ2eZm5vj9OnTPPXUU7zwwgucO3eOq1evJmV+nudx9epV7t27x3PPPcfExMSuX8th0M/hjXs5sZ8dyXFjodUVIJWiqaF3V/MMXSEIJYsNm7YbXTyVs5v39K2nX3a3Rz2rJOVwePbZZ/npn/5parXamvlwscPpwsLCmiHqrutiGAbvn9T5hp+h6ak0FJ2soW+iSZkNmiSESIwceg0geo0d+u2w9rh9E2EYEtot3No8rWYTNA/f7mxa1i6EAgJCoWJoKsV8llKpRKFQSM618fdxZmU9nufxrXtL/OvffIt6Pk9ouOiuh+97hG6H0G6jEH3GLS8aeKoCSwvzydwuRdXIFErkhk+gGVlAsrBco6QUeGZc433PTCavrdPpYFkW7XabsZkmqzPLLLaWKGRNcsMTlE9eQKgajmMzv7SKXV9GOi0Cx0JxGoStKlZtGRns0oQkDGgt3Ke1cD/5lTsxyb+bu8J3T59OBtwPDQ2tKemL5xjGQVWpVGJsbIyRkRGGh4eTEn5FURJTiueee44wDKnX6zx8+JA7d+4kQVXcSiClTBYTr1+/zvDwcBJIxa0Wo6OjyRytw6Rf+rTXoGOpFV2HdlyfoZzORDGP5QdJQLXa8Qi6i6VGt+x8K/ptwQ6Dp09pEHVIXH+4yK999vM4foihKZwZzm/75ot58VSFclbna7dXuLfcxvICQikxNIVKNsOFiRIff+YkVyYra8oa4oBo/febGS04jkOj0YjmUzUaLC4u0mw2N+1Z6uX+SpvPvj3HQsMml9E4PVzGyOajEg8zh8hkqbsKTTfkiyt5Plqe4swOKycH4U4TC+m1uQYdN0ARIhogKMHyAtwgpO36TFWym6aAy+Xo9tdLJ/m537rK7bkqUniUtIDQtbAbVQJPkCkM4WgFcqXTjGg+HztrkiW6+Gi326jZBp17KzQ7DpYXYEoHz+kgQ9mtXTcQpTy58hQFVQUJim+hBzZOK8pYWatLOM3qtvswQYb4rVX81irOwl1W3v4SC1+e4JlnnuGDH/wg2WyW+/fvY1nWGqFvNBpcu3aNkydPcvbsWYaGhpJg6saNG0mZn5SSBw8eMDMzw+XLl7lw4QIf+tCHWFhY4OrVq0mdervd5vXXX6dSqXD58uWBGNrbSz+GN+7lxF7K6gzldFqOj5RQ60SP84MQr/2oXGOiZGJ7IbtddO+X3e1RzypJORwUReH33/g2d+ZWaHdsZOAyZCiUstFlgWEY5PN5yuVyUvqlqiovAs+stDdqkq4xWslxZWqEH3rfWV48N54ES7lcDsMwDvVzv9e+iZGcztmSQq1Wo1arUa/XI02sWwTLD6g3bHLb9ESpGQPVyCNVOHlyjP/bj30vl6dG9vSadV3ns9dWmbFUfLMCgAwlmaScKoxc+JwWgdVEeG00EXY/ey5FU4fAx65XsetVQilp2j6OYnB2cpw/90Ov8r7z48mibq+N+I8Vx/hXrz9gvtokwCej+Ph2G68bNGY0hWDoJFKG5ERIWQvRNdCzRULPpbO6QGtphtrCNG5jhdC1tn2tMcsLc3xp4dECXbFY5IUXXuCll17ilVdeiUw01vWqxcN4b9++jRCCcrmcBDy9znyKoiR95vHojkajwZ07d7h58yYPHz6k1WolmcJ41IdhGIyNjVEqlRJHx7i0cHR0lEKhcGjv5cfVp70GHZVclIHKZVRsL+TBamdNOaEXRBb82UxUdp7fpkqi3xbsMHj6lAZRB0xcSvDtuwvcf3uWMJQoiiCrq5wdyfPhSyNrVuviYKd3ftKVKwZ/7GMZljshd6oWnlQp5U1eOjvG2dFHKwtrVixQeW60zFTPh8/3/WTSeW/QtFcr21wuR6lU4t89nMetnOXkyTyTo0PJsMVezByIxu7rUvvtThML6a3FFl4gUdZN4I6b+W0vYKZmoavKling9bXKc45PoTCMVpzCbzepVZfJ5F1OVQL+gysVXpgq43le4r50ybKoTCzw1RuzNNs2lu+T0TUEEi/wcb0QXfjkNJ/zw2Ue1FyagY6ay1EeGqfS3ebQ93DaDZq1aBUwaOw+sIprw3/v934PRVE4f/48L7zwApcvX6bZbCYzSgqFAvfv3+edd95hcnIycTl6//vfT61W4/r160nGKQgCrl+/zr1793jmmWeYmppibGyMe/fucfPmzWSVu1ar8cYbb1AsFnnuuecO1Rb9oNnrib1kapi6wvnRApYbbFqu8ZFLo/yj37vN7aXWgdux93LUs0pSDp53Zur869fv8+tf+A6dThuEimZmyefynDtR5mPPneLixMb3URwQnTlzhv/gwzlqrsL9ukeg6JQLuV2tkh/WTJftSpiklIRuh4LforW6wvXpJv/T7Lf5yfef3vA8Y0WDsyN5ah2Xpu1RzmbQzCyZbCEyesoW0LN5FFVjsWEzEkg+8OwEV06N7nmbH1Y7vH53hVrHAySaqpDNqI8a+1UFo1zBC0r4QYiUMGKG5LFZXl5hvr6KoQQbMoOjZsDHToI1f5uvz99OzBbGxsa4ePEiAM80myi5Cv/qK99lpdGh5oeY+TJqHly7jV+rIe0GYeCjZlT0XBFFUQm6peh6vkzWKKGdfZnRUp6PXyjSXHgQlXo/mGZhYR7X2dkpsNls8rWvfY2vfe1rAInufPjDH+all17Ctu0NfXNx4Hv79m0URWF0dJSJiQkmJibW9J/HAVfcT9XpdHj48CHXr19PFo+bzSaO4zA9PZ08ptcUJR6HEpcWjoyMHHn533bsJ+gwNIU//tJJVjvemnLCkqnheAFeIDkzlKOY3TqE6Kcm9TJo+pQGUQdIbylBy3KQmQKZTIZA0akFAi/I0los8hefu8Ifef4UhmFsW+t9Hnj/Jr9fb1vp+wH4Nlk8zpcVPnwmz3hW0ul09rT9hmEk9ujFYjEpTdA0jYfVDvN/6OGbBhOjBcQ2H9C91qX2050mFtKMKrCFAIU1K0gCyKgKLtHcg6Wmw7nR3JYp4K1qlYvlClMTY0mt8rOTRdrtNo1Gg+XlZd5+9x6z8zXGzpb4+MhpHixUmV2u0W638OwOOpJiDk5Ucjw7VWailOXOUos37i7TdHxqrkI2l0XPZAmFim+OUR49z5QOHzpXZswIefDgAQsLC8zMzGxwLNqMMAy5ffs2t2/fBqILpLic4uzZs5TLZXRd58aNG7z++uuUy2WuXLnCiy++yIc+9CGWlpa4evUqzWYTiGaNfOtb3+LOnTs899xzXLhwgVOnTnHjxg0ePHiQNEw3m02+8Y1vMDExwXPPPZc0Nx9n9nNiz2U0fvqDZ3juZHnLi8qvvLt8oHbsm3GYs7RSDp81JW7ZcYyCJJMx8ENJ3Qt5t6lSv9nhp8dP8YnnzyS24+t7pGI206TNOMyZLutLmHzHwu20kl5St9NMBr7qUrLctLkrTJaazoYKkXw+z5/8yPPU3lhgzlJRy2VGy7kNmrTYsPfl6NnL1dk6s3UbSeR6tpnFdKxZQNT4Lwz+4489y1zD5u3pOtV6HbfdILBajAUWUwXB915cu1jrui7v3HrAg+q1pDrm8uQwr52b4NT4R/jd60tcn63RaEb7StNURoeGyBsqSys1Go0ay/U6uhKZEAQSfNUga+YYzWf44Ok8RdVn/OJFXnnlFRRFwXVd7t+/z82bN3nw4AGzs7MsLy9veH3rmZ6eZnp6ms985jNkMhleeeUVPvnJT/LRj34U0zRpNBprgqowDBN3vrfffptSqcTExAQnTpygXC6v0f9cLsdTTz3FU089RbVa5eHDh8zMzCTBVHyLZ6jFC4e9gVUul2N4eJgzZ84kQdVhZqp2Yr9Bx5WJIj/4/OSGRY/PX1vkG3dWsDx/2yCqn5rUy6DpUxpEHRDrSwkunyijnnwt+XscCMzZPr/45hLnT47z/NTenHyklPz2N+/xDz//NnNLq7RaLTLSRngOfhhieyF3NYVvvRPVp794qrLp86iqmgRLvV+3c1Y7yLrUfrnT9AppJadTt/0NUzBjdFXBcgPajo/jhdumgHdbq1wsFrnfCPnM/RpvL45SrSnY7QahZaNLk7NnTjFVyVI0NKRnUVZ91CCaGxIEAedGcpTzU1yb7zDfcHA8D7dloQDDxRLnp8b4+HNnuXiiAsAHPvCBNX1sDx8+5Nq1a9y+fZtbt25Rr9e33e+WZXH9+nWuX78ORM5cJ06cYGpqisnJSQzD4OrVq/zWb/0WJ06c4IUXXuDFF1/E8zxu376N40QrkvV6na997WtMTESlg3Gm6+7du9y7dy9x8ltYWGBpaYlz585x5cqVLed5HQce58S+XbnGQdqxb8VhzNJKORrW69LTzz4XlTcbWTTDRGgm1Y5Hw/b53GyGD746woUTj3/xcZgzXWzb5itvvcvc/VuEzQaLyx7hNq6oihCYuoLlBSx0Ql566iTlcplKpZIsJAGMnVvsu6Pneh6uWthuNJBX32FYrK4quL6P7QYIRfCzP/rcppp0oqgnpXurq6t8691pvnR9nvsr3VLMuDrm6gJnRx7w4Usj/NGpPC+XDGbqoGTGyJkZzlRMhnMq12dW+PKNOe4vd2g2W3h2i9CxUAOX8azHMxNZLoxGgbfv+8nsK0VROHv2LE8//TRBECQLjA8fPmR2dpbZ2dk1+rAZruvyjW98g2984xv8nb/zdxgbG+MjH/kIH/nIR3j66afJZrPJHMOYuPTv3XffxTCMJEM1Nja2pkww7oN6/vnnkx7her2eDJ/ureDxfT8JrADu37/PW2+9lbxvxsbGOHHiRFICeBQ9VTGPG3Ss16dTQznur7QPVZN6GTR9SoOoA6KfFoxSyuRD3Hu7en+BX/rGvW5PksqoqUf12lp00o/qoaO5S599e45KLsNzZyc2BEtbNblux0HXpfbDnaY30ItmHQhCNp9zJYiOiR9K2q6/qxTwTrXKGy8c8mTNAl44SdPy8HFxA4c/cbbMhVJ3ajyPyi4bjQZGvU7FUKhbBktNmwBB3jQ5MZRnOG/g1xe479TRdZ1sNptkDYvFIuPj47z66qvJ9szOzvKtb32Lb3/727z9zlU67dZWmw6QiO61a9fQNI2JiQkmJyeZnJxkfn6eb3/726iqysjICOfOncM0TTzPo1KpkM1mmZ2dZXFxkTNnzvDUU0/xzDPPcOHCBa5fv540CIdhyJ07d5ienubpp5/mzJkzA7OCtxcO6sR+UHbsO3EUwVvKwbNBl0oby9fGSyr9tAY+yJku8QDWuJyrVqth2zZv316mtTiHRBJmN18MVPUMeq5AJltEBjqZXIGnXnmGV7d4Lx+Eo+d6lps2oYwyHTudBQV0Zy1Fj4OtNWlsbIyxsTG+eH2Rzzd9ZjImjdwqRmihOh0cq03dsql1XB5U28mi66OsnITQotWCU2WDn3j1NIv1DneXmrQ7FpoClyaHqWS1RLt63f/iOWCLi4t4noeu64yOjvL000/z9NNP02g0qNVquK7L3Nwc1969w7Ub79JYntvwWnpZWlriM5/5DJ/5zGcAGB0d5ROf+AQf//jHefrpp7Esa83sMcdxEoMKRVGSWZ3Dw8MMDQ2RyWRQVZWpqSmmpqaAyOwjNp+I32PLy8trWiLinqpqtUq1WuXOnTsUCgUqlQpDQ0OUSqU1ZhXlcvnQHGv7rU1HpUm9rNen4XwGISPnakXVQNUPTZ/SIOoA2K8F44OVNiMmG4KlVqu16RDCr9xcYrXtksuoiTNPjJYx0cwc5RM56p5KoGVZrJzjP/v4C315jYdRl/q47jS9gV68vZ4XDd7d7EI9+lXUOPm4KeAdLxy6ToCLtsnnl/L87Iee4+JwJpnGXq1Wk8yR67o0Go3kRO77PgQOjUaU+Wk0GuTzeWzbTr6Pm7jDMExsg0+ePMnJkyc5/dKHUd6aZX5mmurtt7AX7+CszETDirbA931mZmaYmZkBoizbyZMnmZiYwHVdFhcXk0Z013UJgiBpSn/77bf54he/yKVLlxJjiUqlwu3bt2m1WgghcF2Xt956i3v37vH8888zMjKyr/1+lBxU4HEYF2/rGQShTOkvR2UN3K8FRd/3NwRMW5WoG5qCoggcP9InRdWigClXQM8WyeQKqPqjsr1GzULT1R31qZ+OnpsxVjRRBMlA7u0WlOJxEooqGCvuPA4k1qR7Kx1KuRynxoeS85OUEsdqs7SySq3e4At3LUZLeU6WNq8OUFWVyeEik8NRL5DruqyurrKwsEIYhpRKJSqVCu12O7mOkVKiqiqZTAbP83j48GESZI2NjXHmzBlUVaWtlWhVM5ReOI3pBwi7gb08TWv2NoGzfYn68vIyv/Irv8Kv/MqvoKoqH/nIR/gjf+SP8IEPfAAp5Zr+7zAMk6AnplAoJMHO8PAw+Xw+CfhGRx/1uK0PrB4+fMjS0lLyWuMMXKvVYnp6GsMwkplrcUvE0NDQmgDuICsx+q1NB61J8bgf13UTZ9D4a/z9x8qrLN68y0K9wz3bTpyrjfFz+LmxQ9OnNIg6AHYqdZNSRoP57A6e3YGVKvcXHf7Fp2/z8unKrv7HUtPh/kobF5UToyMYuTy6GTnjaUY2isa75EPJrcUWV2ebffPKP8y61P260/QGeqauUjBUvCDECyQZbbPjEq3sdZzgsVPA+7lw+NkffW7DClgcVMUna8dxaLfbyQm83W4jpUxO2DGZTCaxk49FyzAM5ls+/+7aMsuOSmHsJJOnTiNkSKe2SPXedZqLD2ksTOPvMGyx2Wxy48YNbty4gRCC8fFxJicnmZiYoFwuo2kaCwsLzM/PY5om+Xyee/fu8cYbb3D69GlGR0dRFKUrvAuR+1PXSfLWrVucO3eO1157jXL5+FycH2TgcdAXb5vRK5RvPahSa1sEroca+JzU4fJIlv/o+y/xyoXHK79KORyOwhp4v4Hb3cUGZc1f45S3vkxry21XVV68eIqvLajMWAqjpycwsltv/370qR+OnptxaiiLqatdF7TNdSrGC6Igy9RVTg3t3AqwnSYJITBzBU7nChhlGzeQzJcm+HM/cJFms5nMbYp1xrKsNX1ImUwmKZOLnf86nU6yoJbJZLBtOxmEm8lkEnt7TdNYXl6OKhfqbb6z6NHRyhQnz5FVAgKrhTc2gfbyR/Bcm+rMA1YfXMetbr/wFwQBX/rSl/jSl74ERJb+3//938/HPvYxRkZGNn0/xa8vrpQwTTMJoEZHRxOTivWB1auvvkqj0UiMm2ZnZ5NMVWxUEZcIqqq6oVxUCEGxWEws24eHh9dYvD8uB6FNu9UkKSW+7+O6bhIYrf+6WaC02RiBXs4W4E89W+H3bzrcW/Tp2A6O75ItDXPx7PkDWVzcjDSIOgB2KnWrz96h3ZOmDiyXUAgsd2uHF9M015Rq/cHDFrnZISZDwXhl+xPoQXjlD1pd6masD/TGigZtJ8D2A1w/mrHR69LnByGaquAGIWdGcvtOAfdrxVfX9USYYmzbTgKoer3OyspKUlrQaDTodDrJilvvKlvsuPelO03mqx5GvsD4+EQ0p0rVKIyfojx5geb8faz6Mo1mk9byLGpjjtbyXNLvtBlSykQ8gMQednx8nKGhIRqNBqqqUigUmJub4+7du5TLZS5cuJAYWMzPzzM3N5dkXO/du8eXvvQlTp06xenTpzl9+jRDQ0PJrJVBG94bc9ArdP24eAuCYIN49d4eLje4MbtKy7LRZMC5ks7poZAHsk2rYwMOo4bAbAVMZvuT2U45eI7CGng3gVsY+HhWG89qwcoK9+Yt/o/gXV49O7zj8yuKQqlUSi5KK5VK0n/ylneVz19boO4JxreRyEHq63vuZJnJsklzsRUNOF2nU9BdpQ9kdwAqTJZ2Dv72q0lLnZDT3VLAXsIwTAKrer2eWKXHpXrr7+84Dp1Oh1arRbPZZGVlhWazSbVaRUqZzAp70AiptyyEX6WzFOAXymSHxsiPThKGAYqqM3rqHMXTV5B6jrNZl3F/kTfffJM7d+5sO5blu9/9Lt/97nf5B//gHzAxMcEnPvEJPvjBD/Liiy+i6zr1en3Dhbtt24mxBUQmI71BVW/feKlUolQqcfny5aQ6Y35+PslQxa89ft3VahUhBPl8PslSNRoN7t69C0RGT71Zscftq3ocbZJSJrqx2e287uERacrsuw+4ty5Q2iu9gVd8i/u9e7+GYcjTBhSLHR54Np4iuDxu8Bd/8Ck+cP5wqlnSIOoA2KnULRqA9wg/jOY+GZqCYRhrgqX4tj7V+4fL00ixiK5sH63HHIRX/qD3TWwW6J0cMpldtXGDEMsLUZXuJPhQJo5Il8YLj5UCPsgVX9M0MU1zTWDlOE7Sv7SyssLc3BzNZjMZjmxZFq7rMr1Y5fb9eRodj6G8zsLqQ1TNSHoDsuURxp95DRSF5sI09x88ZMRU+JHnRliZe8jVq1eT/qXtiO1h4/vF7kWFQiFxSVpZWWFpaYk333wzcQS8dOkSy8vLSeAnpeThw4c8fPgwWamLa9jjAC2+5XKDY2pwmFmjMAw3BEGxeG0VJG11oXF/pc1Xb61wf6VFu+PguTYELjo+Y1mFyyMZRvPReahtQRuoVqtMTk729TWlHAxHYQ28PnCLAqauQ57Vxus08Xtsr+MFRcffeO7tPQf0BkxbLagMuj5txunhHB+8MMJiy6Fl+0hYo1MSksqPWDs+dHFkx/NKvzVJUZTk2uTEiRNAdL5ut9tJQOX7ftIrFAQBvu/TbrfxfT8JwprNJsvLy1H2ZnGFhw/nsZ2AUiGLomfw7Q61h7cAiWbmyORK6GYeQ4asVBeYy2X4+Pe9nz/5J/8kmqbxne98h6985Su8+eabNBqNLV/jwsICv/zLv8wv//IvAzA5OcnLL7/Ma6+9xksvvcTExATNZnNDABAHjvfvR8OCS6VSElD1DlHOZDKcOnWKU6dOEYYhKysryUJjnKHrDaymp6d5+PDhmrK/MAyxLCspoY9LAOMywEqlsmMJYByMxLepXMjPfGicB8tZvjuzStt20UXI+WGT0XyAu3CbP5j28H0fz/OSx+0nENqOXo3a6qYoCpqmJbNNNU1LWgbin+eaLm/cbzDtFbCyIWEo+W5d4e9+7kbfHT+3Ig2iDoCdSt10M4eq62hGDiWTQxiSU5Oj/Ief+p7EaW0nDkIQ93rRdxz6JjYT0syowlLToeUEBGGYBFC5jMr3Pz3OX/zohcfa1sNe8TUMgxMnTqwRs0ajkQRWq6urvPtgnutv3cfW2qh61GMXeC6B54IFrdUlFh/eQYYSzTDJFcsYGR3Lc1mxQl555RU+/OEPYxgGYRjy1ltv8fWvf50333xzR9e/Xhej69evk8lkGBkZSUwqpJQsLS1x4sQJKpUKIyMjVKtVXNdNRCJ+TY1GgwcPHmAYBuVyORmGWCqV1gRVva5aR8VeskZhGCarenEgtL7kYbPvt3Oy2s3/dBwH27b59p0FvnB1mlqzjWPb6Cpd62KJ50uqmsJc1eBDl8Z5arKCECKpW085Hhy2NbBt2ziNKvbyNO1Wg1DzCdytM9rwaEHR1NU1AVO5XKbua1xfaLHiBuSaKs8VdcrbZKSPgz5tRqxZ7y40CWVkEBWEIJEoCHRdoAiBIuDCLoO/w9CkuNqhUChw+vRGwxKIzuNxBcXKygrX783geSb53CR6rkbOfYBYXURx6nh2J9IpKQlDEI6LUlsmkzHJ5EuowsR2Xd66cZvq/EN0XadUKvFTP/VT/IW/8Be4f/8+X/3qV/nOd77D7Ozstts+NzfH3Nwcn/3sZ4FIU1988UXe//738773vY/z58/jOM6G3vRYk+7cuQNEwaVpmsmMz3jmZyaTIZfLceHCBdrtdmLtHoYhuVyOMAzpdDq0221mZ2e5desWEPUe5/P5pI/q3r170T4JAsIwTGzW4xEEmUwmCVjjYHUrKt0bEtrL0a0fBEGQZIrihbwgCJJsVjyLUtO05JbNZimVSknQpGnajtUmb03X+O2rVWquwJUG+XwW0zAJjSK3l1p9dfzcjjSIOgB2KnUzChVOPPtBABYbNkMFyWtPT+w6gIL+CuLjzO84iqb3vbCVkOYyKkJAw5IUDJXhfIa//LGL/OnXNj/574WjHgYXDxQsl8u09CG+/HCa7zQE9zMK4Ykywm5h2Q20wEbzLDrtBo4XPHIudFrUGm2EANMwqNY0bt6MAiVVVTEMg0qlwqc+9Sn+2l/7a9i2zVe/+lW++MUv8s477+x4YR87MM3NzSXPOTo6yvDwMCdPnuTy5cuMjo4ihGCh2mCx3sEPJbmswYlKgXIug+M4ySwQiMQrrrGPb/HAxXhlr9+OSLEoxCuusXBt9v36Vb34a3zbrgzlcfF9H9u2k5vjOMn/V1WV5bbH71xdomoFZIwCQ8UyhqaiKdGFW+gHtG2PuuPw+r1Vhop5zo5FweuTNDD5SeegSrBj99h6vZ64rDUajWjUQtNB1mZpNGzyZTNyj12HEALNzKEaOcgonJ4a58/9mQ9zbiwqb39nps6/+MqTqU+bsV6zGpaHYSgoQAh4fkgpqzNZNncd/B21JsUIIahUKky3BZ+dt3l7eZxqI4PTbtJsuVi5CVRjiFI2Q6tRpbmyiNdYIQgd8DyQCrZnobca+EGIY2RZzU1xujKJrutYlsW1a9dQVRVN0/jABz7ARz7yEVZWVnjnnXeSGVXbBRcQVVO88cYbvPHGG8nvLl++zKuvvsqLL77IlStXMAyDxYbNg2o7mbV1ZjjPWDcg2olMJkOlUkk+L/V6nTAMURSFfD6P4zhUq1VmZ2exLAtN05K5VKZpblrap+t6EsjGgZW6g1X+blFVFUVRokHVYZjcejOOsfbHBiLx/9+P7tbsgLlWQKhkKBWyPHd6jPMnhshms9ypOnzzwR3sEycYN7Uts8z7cfzcK2kQdUAcdClBvwSxH/M7jqLpvZed/u+WA3JNnalKru9COijD4NYfW6UwgqGWCCUQBoSuheNaKJ6N3qohO6sE7RqhZ+N7DoSSwLZZrrU4X66wsrJCo9Gg7fg0bJ8QgUI0xXykXOCP/tE/yo//+I9z6/40b3/3BtP3brM4+3DH7QyCICl1uHbtGl/4wheissXyKEp+CGEUUA0TRdXQVEFZF5wdKzJWzqOqKqqqJin/+CQPJF8Nw0hWA8vlMsPDw0nQVihE/QG9ohCvlm32df3tqBFCJK89FrT1AVrs0BgHl7217fcW6/zmW/Ms19rI0MMJA5pIVCVydCwV8mSzJsP5IhlfEAiNWVnhY+fOAAxsb1rK5jyuLsUmNr19mYlj6CaMFQ3OjuSpdVyatkclb6AZOfRsgUy2gJ7No2XzKIrKYsNmuCR59amJJIA6rvr0uP9rK81SRBQ87lWzBkWTYLNjmsUcyuFkR9CLLjLwqNttZGaETH4S3WkRdur4nQZ+uw6Bhx/6COmi+DYrCzN8qz6PVHRCLUu2GPUmTQ4XUVU/GcPxPd/zPbzvfe/j/sw871y/QXV5iVZ9lWZtdVfb/e677/Luu+8mJYD58gj5kxcwx8+RGz+HWR4mqymcHcnz4UtrhxtvRdw/NjY2RhiGySJErVaLzD56zCU8z8OyrKSnLA6qYnOO+D5x9QlE+tDbMzg0NEQ+n0fTtCTQjDM/8aJg7y1e4OstDe9FUZRt54luRaxHpmkmuhR/f3fV5f+6usx3l9uPFk2qgn+/2Ob5KY1PvVrmt28sstDy+zJC6HFJg6gD4jBKCR5XEPs9v+OgHIu2Yi8ZtMMU0kEw3djs2HpByC0vxPYCdF3DFnlEJo8ioDB+Bk1VkGGA367hNKq0V+aQnsVSqBKaJXIjGa7XBDOrK1gdh8CxEQpoikI522Jotspqx6VuBwijjHnlI5w+b+PW5gibK/jtVWrVlR1dd4Bu1mQaWNd/JRRmdZN3DYNKIUe5kEtKAkzTTAKp+OSeyWQSwVAUZc2qXFxSUCgUktLA+KtpmsnMkMOgNxiKb5lMJvkav454tS8WtdjWfnl5eU25XxxIxaV/8WphL/eW27z+oMGKDVLPoOpFVN1AaAZSKARC0FQFGTODaWiUpWSubnNvuc1K2+PMeOVQ9k1K/9iLLv2Vj1/kVF5y//79JFhqNBo7ruLHaJpGqVTiz3xilMYfzDPbEailIiOl7AadWWzYG3TqOOrT41R1rKefmjUImgTbH9NSNqDj+FhSh1wFJV8hP6agKoLQ7RB0mritKp2labzmCqFrU8hneObcMHcWVplbWKbTWiFwZxCqimmYTIyN8r7Lp5kaKVJ34GZVMO8N45x4CTVXpWC3KHo2maBDxm9Tq1ZZXlkm9HcuU27XV2jXV+BanK0SaIUh3h6Z5Kvjp/joq8/wvS9cTkrti8VHxmGxPX3vrff3iqJgWVYSEDUajUTX4puUEtu26XQ6yUJinIGKy+FUVV2jGdPT06iquqbkUFVVXNfd9ed6J+KqkN4AaX2gtFWpfRRg39920eSb96tYXnjooxq2Ig2iDpCDLiV43ECtnwOBD5v9rlAeVqB31E3Nmx1bVXlk8+5256fEW+SHoKkgFBW9OILMDaGNn0d1Ggi3wXc9lYZtsVzIEjBGAQ9pt7DqS3Tqq7TrNtM1B1VVkIGP6nmoSgPFLCKHz2KMX2Q4o/Lx8QyLszNcvXmHVnUBv9MAuYeTtwwJ3Q6u22Gxucriuj8rirKmHr03SxUPJM7n82uCq1qtxuzsbJLViksWTdNM+q2KxeKa542fL5fLYRgGuq4nK3u9q3zx/49/v9TyubXcwZWCYs7khVPDnJ8oJyuJUkqazWZia1+tVpNSj9gqt7cc0Pf9HYNSIQTZbBbDMBIhW7JCbi8tYpfKZPIhIMio0btBKCpoGUJFR6oaLTPL0FiFYj5L2AnJZbOceP45vv/51FDiOLJbXXruZInf/u3f3lXGNc7y9t5yuVxSclSZWtyzTh03fepH1mwz+qVZR61JsP0xNXV1Q4lapEsC1cijGnkyQyconH6WVruN11zGdFa41lyh6ug4RgUzP4QIfVyrTafd4v7cIgtLK0yOlqkFGRw1iy8FppklP3GWAGjVVrGby3ScFmJyjMr4FTzbQlpR9sttNwh3mE8VIfFbVVqtKq37V/mlN/4dv9Tz13w+nzjN9rrO9t56A61ePM9jaWkpKWHvdDqJBpRKpSRIqlarzMzMJIYSu1mwjB0Ce8sAe7NLS00nKVk0dZXLk8OcnRjaECTFPxuGsc1/25pdL5qsdPCDkHI2c2ijGrYjDaIOmIPOgOw3UDuqwYv9oN8rlHtht8fxKJuatzu2Y0WDpu3j+pHTk0I0aiMIJaGMBg57QUgQSkxd48z4KR6sdrgrJfqQwokpneF8BoWQMAhQVJV62+Hmnft0Fu8T1uapqB6qdAk8p7uy5uILhYav886KROonMC+OUH4KDBHSXpqmtTSH265ht1tIz9r3a48djSxr++eInX50XU8Cpmw2Sy6XS275fB7TNFlZWUlcgeJs1dDQUOLE5HkeQog1pYW9ARnA9bk6/+7qPDcXWjRtF6/TQXod9MBhMid5dixDSQvodDr7KhOsWx41OwQtQz5ncm58iJMjJbLZLJlMBiFEEmCapsnX/2AGKwulrEonVBGqjmGaKFomCqK6uEGIlNDWM4yVcmSxQRF9dflMOXx2q0vlcnnNMFKIrJd7M7flcjmZn7MVe9Wp46ZPqSbtbju3O6a2t/acEulSSCgV4l0pifRJ0Q0yQ1NI7QxSlZzGwnTrtJemsWtLuFaLYGiYdqdDu9nk3bkaQkBGVcgXCpiZIUzFIJMrMT4+Qc1TWVlt4K08RGvNk9VVROlRQBN4Lu3GKnariWc1kO3dlQD20m63uX79OtevX9/yPtl8nqHRCYZGxjh9coJTJ8aTYbzlcplCoZAMtI9nR8azIjfD9/3E5U4IQRiGG+4bl+halkWtViOTyVAsFukoOd5akczaGVx9GDQDPczw9kKG57Uoq/rsY7xH1r9vP39tcVeLJk3bZ9UN6Li7M1U6CGfqXtIg6pA4yAzIfgK1oxi82C+OYoVyP2UaR9XUvN2xzRsa5ZyO5QVIGYmSJHKAcvzod6oSDXCcqmQpZjVkVWJ7IflM7/5WUFQN2wtYaIeolUkK5RNRaaDfoOIt47uPrItlGLDa7ND0FbT8EGdHh5kYHSL0/WjwtNNhdf4hD+4/wLU7BPVF3Noi2DWk08J3OoSu293axyfuHdqJOPDovfWW2sUreJVKJclKxRmouMZ8erXN29O1yDbc81BFiKoohAgCFO6oCt8yMzw/VeHMaDEpM4wDs7h8I27WjW3uTdNk2Qp5a8Fh1hU4igZCRwsy3K3neLY0wo+9dI6Xz48npY4QiVf1bQWGWoznM8zWbEIpUbWN/U26qmC5AW3Hx/HCA2s4TzkadtKl8fHovdObYdpPDwTsTaeOmz6lmrQzOx3Tjhvpj6YKZAiBlAQSHD9AVUSy2KcqgmxGww9C3CCkaGY4NVYBJhk99zS+a2PXV2guPKC58BCHRULTR/Vt8mqUpbI6LeTsQ4SqkSuVcfUiMjtK7tIHmJicYkK3qN6/TnPhAW67TuA66Lkiqy0HPfDIKBJTCfE6DTq1FexmlbBH7/aL1W5jte8we/8OV7+59f1yuVximNSrPb3VFrEJRbyYF2tHbDiRzWaTjNX60vW3pmt89ttzrLZdbD8km9EwckWEmWdOyTK7MrRv57vN3rehlCw1HbxAcmls+0WTUlZnpe1ieyGOF2Lo2/flHrRmpUHUALLfrNVeArWjGLzYD45ihfJxyjSOoql5p2Ob1VV0VcEPojKusLsyJRCYukLe0BgrGuQNFdsL8ILoJJczHp2E2o7PUjMq/bC9IMpiAYGEll5mbGKSol+nufgQ37YQiko+l2Nl1cJtVJGqzWpnAT1bJJMrkq2MEVZOMTTxCl6njuK2aNdX0dwW4wUN4Tu0V+dpL8xgt1Zx2w3sdovQs9HxCX0P23GQfarrjtltZgtIAp3e2RaKptPyIBQaWiaDnjEQmQxCy6AqKqoQeA6stuGb7QZuZ4ixcn5N7XivIMbBUy6X4+ayze8+qLLqaNg+5LI6uqrhBPCg2ma543Fjvs6f/8glPvrURBLc/eGtZVYbLXKapJCJeg48L1qhXF9OI4guAP1Q0nS8A204Txk8Ll++DETn3Tdn63QeLj72+Ws3OnWc9CnVpN2x0zENQolEogmBqgtsv3tOIpqRJYQg09Wnclbn3kp7gy4BaBmTwtgUhbEpKlfeT+f+HPW5+1CfxWmv4Ao7WpTzOkjPoWMvErIA4S3at9+gUxjCPXueiQvPMHTmKZxWjfbiNMvzM2iyhudYBIGN0ARGcYhMYQhV0xGKiu9YuFYTr9PC6uqTEjh4jr2r0rpd78vu2JB4htR2xD2/8a1UKiXB1/j4OFNTU0xMTDA8PEwYhrx9d57Pvj3HQsMml1GZzMeumjbYNqaUNFfu8J1bKn/7/jXsH3mVDz5zhnK5vGMP8Vbv24bt03EDhBA8WO0wJbNUcpv3TRVNDa17/dJ0PAx96/LBgzZJgTSIGij62ZS6E4NiebpXDnuFsl9lGodpurHTsVWVaM6IpiroaiRWCjBayDBRMtes7HTcIHGF0tXo96sdNxlY7HXLvQQQT4P0A8nDmsXZkSHGr4xhN6p0VhdoLS2hiKhMww1DMqGC245qzlmaoWF5tF2BahbRi2XyU+MoqkY+65MLLUrtM7hnGlj1FexGlZblAJKXThbIKQFfvzlLrdZA8ToI6SM9j8B3CFwb37HxXIfQcyDc/2yl7Yjd/WIXpfVsmJCjqKh6JjFzcDNZrnaqPHv2BKVSiTAMUVWVXC63pvxQCMGdhQa/8e05FpsOuYzKqKmjeBZ0+6FNKWk2PW7MBPz96VssfOBM4hb1+u1llm/MRXNnshm8poPthjiKiq6poCgIoYBQEIqCJ6P3y1w1Q0ZXmRgZp7Vwn5vLKidPnkwcDlOePA5Tk3o5TvqUatLu2I0uCQQh0XtMUwRCCIZzGbLdx+YzGoauUG27G3RpMywvRJglsqefhdPPQuChVmcJqg/x6gv47Tq+3Ua6FhAiZIDbXGHx3Rqrd96KtkvVUHQDJ5QEtguBj0QQChVdU6JqCt8BCWrGpFwZQTPzWG6AlCEvTBY4O2RQr9dZXl5OjCLqjRbVRhvfc5Ghh9yFmcV+aLVatFqtXQVcmqahGTmkbqKaeUwzh6pnUHQD3cyiGVk0M4du5PBFhjvLD/jfVm7zIy9NoShKMq+xUqkwPDycDMNWVZUbCy3+/u/c5MGqTSmX4Xwli6o96oNrOz5+KLG9gJmaha4q5I2Nn2dTVzE1hU4oqXc8RgtbB1EHaZKS7LMDedaUPbNVhN60fWZWLW4vtfjKu0t9m2U0SJane+GwVyiPS3Nz76pix/UxNHXLYxuLmeeF6KoACYaubgig4FF/lK4q5DMabcdndtXG9gNUEZkRuEE0rFgRkQAiwfVl90SYJ18eIVsewa+0WX0wTWg3QfOAtcdEUQTSc/BcB9or+KFECKgXcyjF6ASeHz1JYfw0KCozc/MIp8mFczluLTRQTxQYPyEpGhphGBB4LjL0ExFQFI1G28LptBhWLMpEs22sdgPpWri2RafTwbIsbNs++EGyYUDgWATOo4BrZhFmbr696d17Z2G56HhoqIaJmcuzbGTRsnl0M7plcoUoy0eGlbrP799c4Oz3XADA0BQUReB0zUXKpobtOni+i/QFqsKajJQXRPcLVYVTQ1meyReTYZClUikNop5QDluTejlO+pRq0s48rHZYbjl4QUi15TKczZBdd4Hcq0vRgGFJdpe6tBVxqVjYTQLpeob8qYuIUxejcutOndbyDJ3lGbzlBwTtGvhu5HXU3bwg8Lu3gNCNhsbKMMD1fAKiuU6KqiMUhcC1sX0X0W7iqVly5SFOnTnL+y6eSKoL4hlP//Jrd/nmnXmk08IMLTqtBlZ9FadVI3RtpO8Q+g6u4xC4Dqr0EIGHbT9+2eBWRGXuDaABwI7TroTCQ93gjVKeYqGQ6FNcLlgoFJKRIt9cDLjX1jByecKcwVLP07Qcn3bLQxIZG1lC4E8bTFayCCUKtES8uKcoyLpD4IbU6ir3GgaVgommqgihoOcKqGbhwE1SYtIgagDYbGXJ9gKWmg4tJ8oEtLqzeX7ut67y5XeX+EsfvfhYJ8ZBsTzdK4e5Qnkcmpu3WimudTy8QDJbszk9vLbp29QfufTZfhit8BnaprXFHcdPVvsMXWFu2cINwiiA0pTuBfbaMgUhQFHA9aPSkrwR7Qtd19HL48jSOGNjecqGwG038DpNnHYDI6yjCoEbRtmtuPpByADfibJJMaGUtBo2UyMlnr5ykT9cvk2gB5Q1F2SIoqgIwwQpkWFI6LoEoYUqwVayLJnDeKUijENGBpiKZLKc4aWTBYYNEuvYarWazO2o1+u0Wi3a7TadTicZXhtbiR80YRjSbrdpt9c6RbV2eJzQDO4aJr//f5YoFQvoZpbZlsQVGZqlIhkzh9CyBJj4Rh4yOTQjiyIUQuI+BIWJkskPvzC5Zv5JOifqyeQoNKmX46RPqSZtzXp9qrZdHD/k+kKToVwmKRuHx9OlrVAVQRDG5erROI441BVCoOUr5M0S2omnCEOJ364Rrj4k33yAbC51s0PRBbyOwLMDfClQFZVS3sDUVQLfxbM7BI61xqrcaTfQfIulux5vtReTDE0ul8NB58FKm9CsMDF1lpbMoIoMBT1HQYaEjoW0m2S8JqbfZml5mSE95D94egRd+MnIgU6nk2hSfItL/TqdzsEvBMqQwLVYWbZYWV7e3WOEQMuYUUbLzKNni6hmDhsDMia6mSPUsvjZPJmRIbL5PJqRTQyPQilp121Kpo6BQmPBY3EmxNQVNEWgD08Rlk4eqElKL2kQNQCsX1nqLZeKL4pVIaLJ8G7AF68vMV+399XU18sgWJ6uZ6c67cNcoRz05ubt6uItL8D1QxYaNkEYcno4t+Y1DOczrHa6QZcqGMmvbRaPj70bSExdQYhIuFpO0F0hjIRLFVG9eihBdrNQQggyqoLjh4khgaErmJqSrAhmNRVVU8l2s1QAo2GIP7PISnWVoNNAWk00oknw62naHqamcLqsM7dcpdWxyGUN8rkCnmPhWS38TjsaGtzF9gKalo8fhrh1CGsa2XwBxcjRULO0JCx7Lj/8wiQvXqxE29nj2Bc73cW9Rb7vJ8JVr9d5+PAhDx48YHZ2lsXFRW7cn+X+3BK+YyMCh8B1CX2XwPdBHl7vhvQdXN9hpl1nfUFHY6sHKSoik0XJ5FCMHPlCHuPcSd6uD/OgW1tfqVTQNI0f+qEfOuBXkHLYHJUm9TKI+hSzl8x/L0+6JvWymT4VDA3Li0ZsLLccWo7HqaFc0v+yH13abp+rSm/PL5veL9YwRDTnSS0OcWXyIxRNjTDw8W0L12rhWS3uzy2zWqshPItKQUUlykqFgU/gOjjtOm67TrvTQVUElZyGLkKWlpZYWopyL6ZpsmzDzKKDEyrUb9PNlkXliULVEboBmtENNHLkxs9iFnNMvXCG185UcByHVqtFo9Gg3W6vGY7reR6O4yTzoTqdDu12m1arlQRdcZ+vZVnJ7+r1el/7trZESnzHwncs7PrKtndd6PlezZhoRg7FiHQpHCpz9sQIHVdBhjptzUQ1shTCgEtnz/PSudEDM0npJQ2ijpj1K0vry6WyupKU1uhEF7JeEHJrsfXYVqlHbXnay25r7w9zhXKQm5t3qos/UTK7JRQu1Y5H221Syelrjm3B1HC8AFNXma1bFJw4CAtoWD4ZTWE4n2GybHJvpdMNyKLjEr8nFUVEQhXKKHMkIqGKb34oabs+hp6h5fjd4EvQdLwN5RxCUZgcH8VRcnTcCTJCMJzVOD1iEvouvmPhdNqsrNZx7BZjBcn3XhxhpmYRhhJNESCUpKSNoQkC38O3mrSaDVqtGoEMQYAiwMzo5DRQhQeEOJbHoqzwpVl4/uWn0HN5Gr7Ez6ic3kfT9efemeN/+d2btJothnUXpxkZYjitGnZzlc7qAk5jFafdwO+06LSbELjkNIkI/GQOSHw7FIGLCQOk3SKwWwRAbQl+/+7VDXebmJhgfn7+8LYr5cA5Sk3qZZD0KWY/mf9enmRN6mU7fSqYGjOrFrYX0nYC7iy3GSsYKArb6lJ8zI2uLpm6ynzdZr5uMzW0+T6Ps1AQLe5tttsSDeuWpQshkhJmRdXI5Itk8pHdeSs/Rdh0URQoFzTKeoj0or7bwHfxXIfVegultkreW+W1MciEzprzuG3brK52aK80cQIQuoGqm6gZM8rsSx9cH+m0cFsrOEjaCKyMytc679I8M7zGLTZe1AvDkKYb8rDWoWN74HuM5rXEGn10dDQZuh4708bOr0JEmn5rvs6b91cIfB9DCQmsNp7VxrXb+K5N6Nr4rk3gugSe1Xcjp+0IXJvAtaEZ/dyehfsbJQmA//2/uc2rz1w4lO1Kg6gjZv3K0lLTWVMu1YsANFVB6a7096Pe+SgtT2P26jR0WCuUg9zcvJu6+HOjeVRFULc8srpKVlc3HNuXTlX4znSNt6frLDZtVrrlFhBll/xAstJycf2Qlh3gh2HXqecRmqrghQHdGIr4bRvdTeIFIYsNm4btc2Y4GsC50LDZ7NiZukreULG8AEURWIFkvuWjKyqezNMSBvnJcV582uSvfPwiHzxd4N+8cYuvLF3F9nxKRTP550IAQkFRVKZrNkUnRLoOgdMitNuUTUnBeHQKLALLLYcb797mv/7/3KdcKaPmymRLwwwPDfHCqcqeGukfZU11skNDFEZOrPl7GAbRipxt4Vpt7sytMKbZ/PizZTLSodFoUKvVcByHarPDwkqDVrvNcq3BQrWe1Ml7roPnuoRhgAyDKAAKfPDdvQ0y3gf5fH7nO6UcK45ak3oZBH2KeZzM/3tBk3rZTp+GchkyqsJS00myTi3HZ6JkbKpL8TEX3R6oWJfqnkvb9albHnXL4/RQjmL20fk8CCWrbTeqjiDSI9eP+oB7ez43W5yKg6je51ppOTh+yNmRHBlNoW55PGhJCmbUf+qFkpbqk69oXHzR5Gc+cYnvuzjMysoKDx48YG5ujpWVFWzb5pu357nj3cN3LITvooQeQXuVgCioUjQDoendcsQoU+UGEk0lCcZarUeF3ItNm2uzTRabNp5UQNVQNZ2srjFRMnjxVInxcoYgCNZkqeKB7a7r4nkeBi6K26HdsVENDaFqZIoVMqUhBN2FUqISeRSFlh2Q1RVOlnRC30F6HpoIIfCSER++77NSb7JcbeBZzQMzdurlhYuHl5VOg6gjpndlyfaCDeVS64kvTHOGSq3j9aXe+agsT2H/TkOHsUI5qM3Ne6mLP1nJ0nEDJkoGP/H+M+Qy6oZj+2MvT/Gv33jIP/7KbRQRlemUszqmruCFkvmGjdJd0Qy7AxAhei8mszuEiNzelMhoIpBhsgq42HAYymeS44Fkx2N3ZaLA1FCO1ba744XT9714mV+/1ub2UovcWGHDcbK9ANdqI5SAbEnB9kKyusrpio506lE2qFWn0bHpOD6OH+IEHkq9TqbdoLHwgAeB4FZlmDfemeC/+GOv8oMvnd3xOO2UNVUUlUy2QCZboKUWGb8wwQ88O8Ff/ZFn6XQ6NJtN/vDmDL/+9e/y7u2H1PRlfKONPzRMJusRysjute0EBDJEIFCEhDBEBn4UUAkFH0AKRvIaHzpfIRO6SY/X4kqNxdWoJMRzLULXIfR2P4+rVCrt6n4px4dB0KRejlKfYvqR+X+SNamX3ehT3tDIGxrjjs/tpTbDOZ3/6HvO8bErY2t0KT7mf3h/ld+7sYjrR/MMDV0hoykUDI3VjkfbDbix0GQop1M0tWSfK0Jg6Aq6qkSusEGI5YVdJ8DoLBfvR1OLjhVEmuV3PwObHb+xwu4D+4mJCSYmJoAoAFpaWqL4rZv87vxXkbaDqT3K7EopkYEXGUsEHkJRCYWC60fljWdOn+DEUG5NRunmfJ2v36nT7Di4vo+uClTfw7U7NHxJtSp4OL/Ia2eHOTeaR9f1xO1VSpm4yvq+z8hIyJyX5e5yG1WGmDpI3ycMAsLQRwY+YeBHA9jbFp7rEnQCbjckomuwoWsa5XyZs2NFKrmoHNPyAr4506HhKwhVQ5ESEfrI0Ef6LqFrETg2gWsRdE01CDwIXELP2ZNzYT6f3/csu/2QBlFHTO/KUixeveVS65HdnhNdVXZV77wX4em1PI0f98a96oEK1n6dhg5jhXJQm5v3Uxfv+CGjhQw/+Pzkhvu8M1PnV958SK3jMZTTt8zsuV5A0A2kBGyY3VEwNFqO173oCvGkJKMpXBgr8Nq5oTXHY7fHbjfv352OU+/nyu9+zRsauXwW8lnywydo2R6L04ugVVHbNYTTIZtRk0xVKCXNdpUb313if5y+xezHn+GDz11gbGyMkZGRLedj7CdrKoQgn8/z+sM2/8d3HeaYwjk1wehFFTVwsaw2C3MLuM0V2q1VZFhH8exuqYoANRIqiAZWat0Byp5QmOmo/LFXnmV8fJyTJ08yNjbG0NAQSy2P6/MNao0m0rUpyDZhezWp5W80GjSbzcQuN25m/uAHP7jt+y/l+DFImjQo9Cvz/6RqUi970aecoTFcyKBrCqOFzKbn9rrl8c9+/y61jkfJ1Dg9nE2ed7wEDctjetWi4wa0HB9TV8lmVCaKJudGc3x3tsFc3WaybFJtu8migESiINB1hYKhMpzP8HDVShwBHT/Y9vjtJ7DXdZ2TJ08yWhWULztQr6PYdYTdILBaUVmdlkHRHgUBfijRQolpZpg49xQffv5M0p/79oNlfud3rhJMjDEsfPK6RIQBMgyRMiQIfJqWS8d2udbSuXh+lImymQRhcQAVf+95Hs9NBSw2LOotlzCQmFpkrKHpJoqiglBpOCFS99BlgCJ9dCHBtfAci1anQ7tVZ3l5iVPDeUZLOXRdx2nV8T2JUFX0bt+XkjFR8mWE8igU8YJoEbCow8unSziuh5AhJVPFUKLsluu6OI6D4ziJo65lWTiOw/Dw8J7fs49DGkQdMb0rS5oSreZvddqJV00yemTr6QfhlvXO+53vcRBzQbY60Tyu09BhrFAOYnNzv+vidx/IwmLTiTIghpaIZDy7A2C0mMH2AuZqFm4ged+ZCv/DH39+w/HY7bHb7SyT7Y5TLJiBlAgZlQyOFdfOllhuuYSZPNnRPIydhsAjr7nkZBunWQPPpZzNAC6rbZfPv/2AsSzcuXMHRVEYHh5mbGyM8fFxisVicsG536zp1ivfOaDC6Og4txZbkb28lBjSAauG36zit1bwWjXcTgMpI2e9kqnTcnwW6hbTi1WazSa3b98GotkghUKBkZERnjp5kpMnTzEyMkKpVCKXi/a94ziJ9Xv81bZtnnnmmR2PTcrx4r2gSb3sdA7qd+b/cRlETerlsPWplNV5Nqszs9qh44acG83z0x88k+zzv/WbV/n8tQVsL+DcaB7bC9YsDuQyKqaustiwKWd1fuDZCf7Ch8/v6ppiv7O2Om5A0dSxvSJhtoCqnMIkIGzX8Nt1AqtB4LmJRbsqBKYiWVpe4eHDaL8KIfiNd1apWiEjk6c5eWIMRYncAgO326flOVRcm+XVBrbjcLOl8eozJ7fdtpeA01dqfPbtOaotB9vzyagSJfBwHZtOu4OLg9Agq+mUsjqKqqIIFaEqSBTathcZVigeZ4Y0AtdGKC0Iu7btRJUYQlEQioqiZ1CMHGhZUHUULYOR0zl/coJSViMMw0RzbNuOnBU1jVKptGa4vaZpfPSjH93z8Xgc0iDqiOldWeo4fjJsbjO8YK3tp9fevN55v9PMH2cK+mbsJH4ny2ZfnIYOcmjgIDY397Mufq+B7FLLRUHiBtE0+XI2s0HAG5ZHIOHSeIG//gNPbXts+nXstjtOddvH9UOEEGQzKlOV7JohfutLltxAoqgZckNlhvJTAHh2G6e5il5f5d7sAveW2yw1HcaKBmEYsry8zPLyMteuXcMwDEZHRxkbG2NsbGxfWdPdXDiMFg3majYIgaeYZMonyVROokvIhJKC9FGsVSqyjWbXkAvzeHaLpWZ0sRDj+35i4x4HVrlcjnw+T6VSYXJykhMnTvDss88yNDT02McqZbB5kjWpl90GZ/3O/D8ug6hJvRyVPp0oZ7m12MJygzVBz2ZB53D+0f8KQpn07MZB50EPIo6D65KpYfuRW6ETKqi5EdT8CIoEHAvVbqK4LRSniaHJNU61iw2b6/fnqDVssvYy8yt30MwsmVwJI19CzxXJmVEZYWlKcmuxhTOa5+lXnmYkK9ZkcOLFMcuy8DyPF09VKGd1vnZ7hXvLbSwvIJQmZr6Emo8cd3OGymjBiJwJPZfAi0rBA88hn/EJPQUHg2amwqmJLHmrgGLZeE60EBtKiUAghESGEj/wEUEHVVXQDROpZAlzFV584UJSegiPeths26bdbicVEpZlEQTBoZeYp0HUABB/yG8ttpIhc1LKR3WyPBouF6+ib1XvvN8eo35NQY/Zjfjpquj2p+xu1sxROQ0NUnMz9Lcufq8XCJWcThBKchkV2wsHSsC3Ok6lrtuTF0jOrGs+hrXlfghBEIbJynpM7PhXGDuFO3wezevg5coUi9BsNtc8n+M4zMzMJBPii8UiY2Nj/JUPjtH50BmuL7S2XeHc7YVDVlfRVQU3CFGIelPWllgajJ0cTgJGo2FD6POJF8q8OBQyOzvL/Px8NHDYstY0WMdzRpaWlnj33XfJZDI89dRTW5YtpjxZPIma1MtegrNBdMQbNE3q5Sj1abOF1kEMOnv30ZmhHCttl7bj44cSiD5n+Vye/FCZkXyG2brF2bLGD3/sNKa0qFarvHm/iuUFmLqSmD35dmRU1KlG5uCKppHJlcjkihiBTsPSuL3qcnlqcsuytyAIkqDqT1gWd+dXufpgiUa7Q6PV5gvvzLJYbzHctZ9XVA1F1dDNtTpWDAJmVxo0FEGYMdDMAjlVR83naLU7uI6N71iEvgMSFFVF1zSymoofuNj1JW5+1yVceUCpVGJ0dJSRkRGy2WySiRofH2d8PFo8CcMQy7I4c+bMAR21zUmDqAGg90N+ba6B121+1FTxqHFfEZj6o1X0xYa9ab3zfnuM+jkFfbfit9Ry8YMQmd1dE+BROQ3BYDQ3x/SzLn4/FwiGpvDHXzrJareJfJAEfKvj9Plri3zjzgqW528IouJyP8HGlfXNyGgaSqbM2JlLfPyVU9i2nfQOLS0tbRi822w2aTabSenf0NAQF8fGGB0dpVLZaM272wuHxEpeCnRVMJzLkO2uAveWWMZ4oSSrZzhz+jQf6q6QSylpt9tUq9Uk8FtcXEyGNsZWuMVi8VCbdVOOlidNk3rZa3D2A89MDKQj3iBpUi9HrU+bBbCDFnT27iPL8zk3msPxQtqun3y24nN4/Ll6+cIEH3jhcvIci9m7fGH5W2TaDTLCwbOayHBtxjj0fexGFbtRpWO52IrCG+UqU/IphoaGGBoa2uCwqqoq+Xw++f3p06f56Pujv33unTnecG6iOR4nChqh50Tlg56bZKECzyXwXRTPI5fPEWoqarFEYVzH8QOGcxnGfDcqzbOiPirfaoHTAs/C92xcz0dTBFYLllWX5eXlRD9zuRyVSoVKpUKpVKJQKJDNZslms5TL5UN3jE2DqAEh/pD/wpdv88Xri1hemLieZXWVvKExVjQwux+qzeqd99tj9Prdlb5OQd+t+HlByELDZbXjcjrMPtaK1WFx0Gn+3dKvuvj9ll5cmSjyg89PDpyAx6w/TqeGctxfaW+6v2KnJi+UCLF5z1Qv6y+STNPk9OnTnD59GikljUYjCaiq1SphzyyNMAxZWVlhZSUaMqhpGqOjo0n5X6FQ2PWFQ3zspB8NOc5mtt7urT4/QggKhQKFQiFZwYvL+1ZXV5mfn2d2dpaRkZFttyXlyeNJ0qRe9hqc3VxsDrQj3qBoUi9HrU+bBbCDFnRuXmb4aKFqszLDXkp5MxpUn6swVskShkE006lVx+00cTsNQv+RnbgfSgwFArvNvXv3uHfvHgCZTCYJqIaGhpIh6psRa1NGU9EyBmS21kkApdaGwOOVF8ZZMae5u1AjX9aQgUeuJ+iKjDAkoe/h2R1m5hcoYHFyVEcETqKhYRgmxkbT09NRiX42m5SfG4ZBo9E41GxUGkQNEM9Plfn5n3olsZuO5/MUTI2srlCzXFqrW6ee95v6/sK1hb5NQd9rDfNi00VKth2YB/tzGhqUk+VB0K8ShcctvRhEAd+M7faX5QWJQUNO39gz1Uu8DypZneWWy69/c3rNe0sIQblcplwuc+nSJYIgYGVlhaWlJZaXl2k0Gmuez/d95ufnk4G1pmkyW5N49Tq+UQC2XsU1dZVCd6ZW0HXg24q9fH56A7vLly8n25ny3uNJ0KRe9hPU3VvucG4035fMypOsSb0Mij5txqBo1uPuo437RkVm8ngFkzA3jqEIMtJFcdtYrTpuZ5GKLjkzvDZT47ouCwsLLCxEJYBCCIrF4prAqlCIPit7DWp9FLJmjqfPnaQWmtSVBTxVbPgMxX1Voe+yUG0yOeHwwbNF/sKHTlGtVnn48CHT09PJnC3XdXFdlyAIktLz5eVlNE3j3Llz+zwi+yMNogaQP/P+0zx7srTn1PN+U99N2+9bzfd+emzqlkfN8tBVsesVq+3E6KDdnAaFfpQoHLRl7iBdNFwaL/ADz0zwlVvLzNYsQgmKAkVTJwijz085q1PJ6Vs+x2zN6mZQHX7x6/d2fG+pqrqmbttxHJaXl5NMlW3ba57ftm1ynoO/dDfqGxwbwiyWMQoVjEIJVV+78jecz7DUihYiLDcgyMp9rfjuxFYrkynvDY6zJvWy36Duynhhy0w2bPycfeTSKJ97Zy4572mKwu/fWn7iNamXQdWnQdKk3n30h/dWo4G+XoimCE4N5TaMBumld9/M1qzu0OJH9u2CqNy7YBRQC0VOPH2a778yzI9+ZIrV1VWq1Sq1Wg3PWzuDKa6maDQa3L9/H4is2YeGhsgKE8NrsdgWBCVzT0HtqaHcltlJRdWQQmXVVXBNjQun8vzMjz635nVLKbFtm9nZWWZmZpibm2NhYSExlmi327Tbbcrlw/0cDYwyCiG+F/ibwIeALPAu8M+Bn5dS7qpDUwgxBfwp4IeBZ4BJoAV8E/hHUspfP4BNPxD2k3reb+q72BWUfqTM9yqa+YyKIiCra7jBzkYFAH/rN69uKUZnR3J89u25A3FzGkT6UaJwEJa5gxTIbrYtoZQoQjBZyvJ9l0e5MFbgF758m7vLbRYbm++DeJimogiEF2B4CroiaNo+M6sWt5dafOXdJf7yxy7yp187vem2GIbB1NQUU1OR61+r1UqCqpWVFTzPY6xocHYkT63jstpoUPbspFFYM7MY+TJGoUwmX8b2Qio5vWtFzUA0TT8ppJq0luOqSb3sN6gbLRq7yhqMFw3KWZ1/9Hu3k3ON5QU0LA8QCEEyhPdJ1aReBkmfBkmT1hN5+siuB2bcoSvp8frZlE+9eoqv3lru7puIuDw9ROJ5IZbrI4GxgsGp4SK2mufKlbHu/33UE7u6ukqtVttQLQHRkODFxUUACo0HuA9XubWYZ3x0hEy+SCZXRDNya2bJrQ9qT8NjZd7i0r2LFy9y8eJFgKQfeXFxkaWlJTzPO/TZhQMRRAkh/gTwa4ANfBqoAj8K/D3gw8Cf3uVT/RfAfwPcBb4IzANniUTsjwgh/p6U8q/1d+sPlr2knveb+v7kMxO8eb/Wl5T5fkSzaOp86pVTzDXsbVeslpoOP/dbV7cMkB5UO3TcaEr5aCHTVzenQWW9OL3/3PCeV9X67V50kLbEe2WrbfFDScv2cPwQ2w/4mRPFbfdBrePhdN/Twzmd08M5bC9gqenQcqKhjC3Hp255/He/8Q6/9Af3+eHnJ/mhFya3PR5xP9K5c+eQUlKv11leXuZHZJaHtW8zX+sALkVTRxEicV9qLs/RtD0cMpw5OcFPft+z3G0pXF+0j7xp+kkg1aStOW6a1Mvj9NjslFmp5HTm6jbX55vJuSYMJcstF88Pu0YcketnnO1+L2hSHDDtN9vTD30aJE3aabuyuoYXSqZXLVY7CztuV+8nI3JYjWxaw7Drqtn923LL4Z985Q6/9dYsr54dToLG7Xpi41uvWdKHL43woNpmodEkcDqJNimqip4toGaLtGWGjshy8URlTVDbb4OP9f3I9XqdYrG4l0Pw2Bx5ECWEKAH/BAiAj0sp/7D7+/8O+PfAp4QQPyml/OVdPN3r3ef40rr/8QzwDeCvCiF+SUr5Zl9fxICw39T3B86P9C1lvl/RjC82tzoB78ZR6cZ8k44boKsKeUPf8L/36+Y0iPR7Va1fJ7eDtCXeK3vdlp/90ef42R99btN94OghUkI5q3N6OMtqx2V21cbt2jyLrmiFEmw/5DvTdW7MN/mVN6f58KXRXR0PIUTiOnTp0iVOXHqe//Wz32JmfpGlWhU9sFCFxA8lthdiagqjecnHpwTj7hzjGXh+UqUaZNBzZcbHRnjlwomBqP8/TqSa1D8GQZN6edwem60yK5qi8Atfvs1Cw15zrrm33EYAGU1BCHD8kJma1dUoNdWkXfI4+jRImtTv7frVN6fpeAETJZMglIlNehCG+IFcM90tkLDSdql2PO4ut3n97gr/9Q8+vSE46+2JjWm320lAVS6v0rB8/u3bs6y2XebqNqauoCkCv9HB9uYxNYWhfIYPnb+It5jlnhf1VhWLxQMz+Ij187A58iAK+BQwBvxiLFYAUkpbCPE3gS8AfwXYUbC2Ko2QUl4TQnwa+E+BjwNPpGDB/lPf/UqZP24N81YrVjs5KnlBSOzuKaVkqemQNzb/QO7VzWnQOKhVtX6c3A7Klng/7GdbfrZbh927Dzquz6ffmGZ6tcPJiknb8ZldtbH9AFUIdEXgBiG9a9tSRhdMd5fb1C1vX8fjk8+eYKL8vcmFQ71j43aa+J0GGa/DyVzA91wY5uzIo0bhvBqQVy3wLZib50btDssjI4x0b4dt/3pMSTWpjxy1JvXSrx6b9Tr1t37z6oZzzfoh3kIIXKLBquv1KdWkndmvPg2SJvVzu3pNUi51AzDHC1lpO8zV7DUBlBCRJkkJQkSLcDcXWvw//+21XQWNseX5qVPRZ+zDHw746LUH/OuvXuedO7PU66v4nouhKVSyGc6N5vneiyOcHc4wPT3N9PR09JpUlXK5nBhWfPzSEKa59WfwODAIQdT3d79+bpO/fRnoAN8rhDCklM5j/J+4e+6Jtpnab+q7nyVd/Ra/3TgqxbXumqoQdldkHC/cdNbPXtycBo3DWFXbb+nFfu2MD+Ki4XG3pXcffO6dORw/SJrRl5oObhCiiqhx1/aCKIAXoIhoEKngUW267QX7Ph7bXTicLBusrq4mlumrq6tr7NQBLMtaI2KGYTAyMsLw8DAjIyMUi8U1dewpQKpJfWUQNKmXw9Kn3iHe8WdMVxUsN9igT6km7Z696NMgaVK/t2szkxRDV3qG9q4dwB52O610JXrPeUHI/ZUOv/Dl2/z8T72yp+1XVZWPPH+ejzx/PtGm1UaL0G4xYQaYoU29Xt+gR0EQUK1WqVarye+y2ewaJ8ByuYyibD6jcRAZhCDqqe7Xm+v/IKX0hRB3geeAC8C1/fyDbnnGjxNd3/zOLh+z1crg0/vZhsNkv6nvfpV09Vv8duOoFDvSKIDo9ry0XR9D33xA6GFMlT8IBnVVDfozXX4Qt6W3GX396rLjdzNQgmRqfNwNrCoiKqmQkNXVxzoeW1049JZdhGFIrVZLgqpqtUoQrH1/O47D7Owss7OzQOS6NDw8nNwqlcqxErADItWkPnPUmtTLYelT7xDvmHhxZTN9SjWp/wySJvV7uzYzSbG9gIbtJwt5Ss8Cmej5JqMqSKJA6g/uVB8raNxKm8IwpF6vr+mtsixrw/0sy8KyrESTFEVJytqHh4cZGhrsbNUgBFHxp6m+xd/j31f28+QiWgL6p8AE8A+llPsSvePGflPf2z0Oog//zYXmjs/VT/HbjaNStOrfXW0RAFFT5VYc1FT5g7RPHdRVtZh+TZcftG3pbUbvXV2WsnuhJKMMVEwsYJHFbHfIoa6w0nIP9HgoipIEQ5cvX05ELA6oqtXqBjtbz/PWzAiJBSzOVg0NDaHrW1u+P6GkmnQAHLUm9XIY+tSrSb1spU8HpUlb0Q+tSjXp6LZrM5OUuuUl76v1BQa9ugSQUQVeAA3b40s3F/mzHzr3OC9pA4qiJNmlGNu215hW1Gq1DQt9YRgmenXnzh1gsLNVfQmihBD3iByHdssvSSn/bD/+9y74n4mclL4C7NoFSUr56ma/764G7i33eYTstzSr93HvzNT5Z79/d88No/1qINyNo1J8H88LUQSoirJt1qrfU+UPwz51UFfVYnY6TrYXrAlCLC+gaOoHctHQz0n3vc3okZV4tLocyKhxVwjWlsTJ6GdVEchQElvVHvbxWC9iUkqazeaaTJXjrK1G6xWwmGKxmJT/DQ0NkcsNfqlRqkmDy1FqUi8HrU+9miSlTM4Rsuf8EHMQmrQV/dSq465J64l1oOP6a+Z89XuWVD/0aTOTlI4bRO8v2Fimve59J4RAEYIwlNxabPXrpW2LaZqcOHGCEydORJvUnUkVz61aXV2l0+lseNxm2aq4t+qos1X9ykTdJrKC3S2zPd/Hq3pbfWrj39f2uE0IIf4u8FeJ6tj/2GPWr78n6UfD6ONOCN+No5KpqxQMFTcI8YMQQxPkM5u/vfczNHY7Dss+dVBX1WK2Ok5tx0+swHtLXNxAEoSgHcCKUj8n3fc2o3ccf9PV5RjZVTBVEVF/VI9w6fJoy3WEEJRKJUqlEufPnwci16XeTFW73d7wuGazSbPZTAYvmqaZCNfExMSgmlWkmvSE0u/z7UHpU6xJXhDiBZKMFs1yC0JJpmt1HtNvTdqKfu+746pJmxHEoyz0kE+/MY3jBweyGLqf7dpMnzYzSYmfZb06rdelQUEIQblcplwuc+7cOSAqOe8tAdwqWxX/fX226sSJE8kcxsOgL0GUlPKTj/HwG8BrwBXWORQJITTgPFHj7Z29PKkQ4u8B/xXRbI4fkVJuDG9TtmVQrEF366g0VjRY7UTlSkKApq49W+zHzWkn9rOPyll9Xyuf/cyuHASbHaf1VuCx2YIXSmRXTH/hy7dRVdHX+Rz9nnQfN6PfWmwRyqgUR1unRlJ2S/sUga5uvGBabjuHejx2Q+y6FM8IsW07WRWsVqvU6/Xu7JFHxFPj41XBCxcuHPp270SqSU8mg6JJvWx3rhkrGrSdANsPcP0QSXQOzBsahq4ciCZtxV73XRhKvCDcVqeOoyZtxcNqB8eLRllMr3YOdJZUv/RpvUmK0bXSl5J4BW+DLsVIIJSRGdel8cOdrbQdhmGsyVaFYUiz2dx1tkrX9eMXRD0m/x74aeAHgX+17m8fBXLAl3e7YtetN/8HwM8Avwv8CSnlxm62lB0ZpIbR3TgqtR0fQ1PQVYGpq31zc9qOveyj+ysd/utf/Q75jLavMor9rF5VsjrLLZdf/+b0gZQlrKf3ODl+h7oVDbRVhSCrKyAEXhAiBOR0lXJWP7ALnn66cPU2o1+baySry7FWBTLqxVMUgaFFpaRuEA3YNHSFhu2x0nKZLJsM5zc3OzkstitfMk2TyclJJicngUeDF+OganV1Fd9/ZCbXW+/+BJFq0oAySJrUy1bnmryhcXLIZGbVwvaihSRdizLTMzXrQDRpK3a77xy/w7W5Bn/j19+mktO31anHzagcZA9xzG50YLZmsdyKBsqWszonK+aBB+f90Kf1JikN61HPayBBdDNQvboE0UxD249K/zKqwpWJ7fvZjoKN741xXtlFtuqwNWkQgqhfBf4n4CeFED/fM9jQBP529z7/qPcBQogccAboSCkf9PxeAP8Y+E+A3wb+lJRyLyUdKV0GrWF0t45KT50o8sMvTPKg2umbm9NW7GUfqYrCStui1vEwdYVyTt9XWeRuV69maxZeELLQcPjFr987sLKE9awPNjpuEDkEKXH5XhRYmLrKVCVLJaez2DiYC55+u3DFzei/8OXbfPH6IpYXRiLVRVeiAF7pBlB+ECJEVKdetzxkd9jh3/3cjQM9Bluxn16I9YMX476qOKAql4/nYNAdSDVpABk0Teplp3ONEAKza2dezmbIZhQUIfquSVux23232nGpWx4dN8DyQjQl2u6tdGq/GZW65fHPfvPqgfYQx+xGB7wgRFEEw7lomPp6DiI475c+rTdJuT7foO0EUUmfAE0RZNQogApCGWlT1xAJoozUUWnSZuxWp7bqreodEnwYHHkQJaVsCCH+UyLh+j0hxC8DVeCPE1nN/irw6XUP+wBRScSXiAYVxvz3RGJlAd8G/h+bzED5tpTyN/r6Ip5ABrFhdK+OSge9yrXbfdR2fObrdpRWFzBeMhgtGMnf97LKtZvVq4fVDsstF0URCC/A8JQDK0vYjE88PU4oJX/j199OhDhudM3oCnlDY6xokDeiko6DvODpt0Xy81Nlfv6nXuFfv/GQf/yV2yw0HNpOlJkJkXiBJOwmamKRsr1ohUxXFUxN5fZS68CPwXr61QvR21cV17A/aaSaNJgMoib1su25ZiI613zk0uiOJXIHwW72XTxE3PFDFCHQuvswzpxvpVN7zaicGc7xc7919cB7iHvZ7thUsjoLDQfhBTsei35rVT/HysQmKb/99hz/7Kt3WW46QGQc4Ytu8NStnojR1ah3/Cg0aTP2q1O9vVWHzZEHUQBSyt8QQnwM+G+JZmeYwC0i56L/Ta4vyt+a892vWeBvbHGf/wP4jf1v7XuDQW0Y3Yuj0uM2DO/EbvdRPJxVUSJhWv9u3ssq106rV7WOh9OtT49W1XIbBG2+bnF9vsn/8FtXeXe+yQ+9MNn3/eQFIZWcnghxvJqUz2gbBiAf9AVPv1y4evkz7z/NsydL/No3p/nKu8tMr3bwA4kvJUpch060QqYpAqMn83bYfRuD2Ecy6KSaNHgMqib1chDnmn6wm33XO0QcJXJ467Vh30qn9pJR+eEXJvns23MbzkWxc6umCDqOz63FVt/PRVsdm+WWyy9+/R6Gt7Wjb+8+6LdW9fM9c3o4x1/82EUuTxT5f//ODe6vdLC8AD8ICeUjwwkFMDMqZ4ZzR6JJm3FcdWoggigAKeVXgR/e5X1/D9jwbpdS/nngz/dzu96rHIeG0aOe6r6bfdQ7nDU6H4gtT9S7XeXabvXK0aPG2HJ2Y1lCr0ue4wc8rFr8wy/d5ne+u9D3VH4s2qau7KoH6CAveNaL0/vPDfdd/H777Tm+cmuZ2ZrFUtPpljGCqakbMm+H3bcxqH0kg06qSYPFoGtSL4OgT73sZvxE7xBxN5AbbNhjNtOp3WZU1p+LNndujbTg2lyDX/jybX7+p/rr3r/+2Pz6N6cHPjjfK73H4w/vrXJnqYXlBShE5ZlFUz9STdqM46pTAxNEpQwW/bSIflLZzT6KgwlFidLqhrK19fpeVrk2W73quD6ffmOa6dUOJytrT0LrXfIURRCEIR0n4OZCs++p/EG44DmM2V3waPXvL37sIq/fXeFv/PrbzNVtTlZMioa+IfMWcxh9G4PcR5KSshdSTdo/O+273vl9iEgb1tuwx2ylUztlVNafi7Zybg27g4i9IOSL1xf5lT98yJ9+7fSB7Zuj1qqD0qn4eAyaJm3GcdapNIhK2ZR+W0Q/iexmH8Wra6Fca227FXtd5epdVfvcO3M4frCh7j2udbf9IHHJE0LgAIoQjJcMwlD2NU1+1Bc8hzW7az3VtouqCEYKmTV9b5txGH0bg95HkpKyW1JN2j877bve+X1e11V0O63aTqe2ysL1notsL9hUk2KklFheiOWF/MKXb/PMZOnAsg5HqVWHoVODpkmbcZx1qv9TLlOeGD716ikmyyYN22exYa+pj4bohLLYsGnYPpNl80BnXAwqO+0jiPZTGEoymsJYcfuTmBdGPTX7WeXaqu69t9Y9oz0Sq+hL5NIzXjIpmVqSJn9cYtHOGxorre2doPt9wdNbW51RBZfGC0xVsoyXTKYqWS6NF8ioIgka35mp7/yku2TQ+jYGbXtSUh6HVJP2z3b7Ls4C+WGUBdpJq/ajU73noq00KUYIgaZGg2FXWm5fNGkrjkqrDkunjoMGHIdt3Io0iErZkrhh9PxoHjeQ3FpsMVOzWGzYzNQsbi22cAN5KDMuBpWd9tFS00EgUIRgsmwmNcibEa9ylUx9X6tccVmC1yOOvbXu+rrhw7LrgRqv/IwUDNqOn6TJH5ejuuBZX1u9fmUrrq3uZ9AYs9kx2I7HCZqP4/akpDwOqSbtn+32XdP2cIOoYsLQFKYq2S21ar869ahsbmtN6kVKUIXA9cO+adJWHIVWHZZOHQcNOA7buBVpOV/KtvTbIvpJZCdr27brs9Jy8YPta64fd5Vrs7KE3lr3NeUSRMLQW/fe7zR5v+c07Yajrq0+6jLGQd+elJTHJdWk/bPVviuaOkEYZQTKWZ1KTt/yOfarU/G5aGbVSsrbN7H7Bx7pU1ZXD6V067C16jB16jhowHHYxq1Ig6iUHRlU29ZBYrt9VLc8fu63rj7WZPLdsFnde2+tey9b1b0/Tpp8s9d+2Bc8j1tb/bjv8UHr2xi07UlJ6QepJu2frfadpij8wpdvc3e5zWKj/zoVn4tuL7VoOX5kpb4FvfqU1ZW+lm5t9Z45TK3ai07Fg4CnVzv8y2/c489+6Nyx1qTjuo1bkQZRKbtm0GxbB5HN9tFpOLRVrvWDD4UQkWVsd0KEJDopR/bj6oa69/04D+3GXehnf/S5Q7ng2W9t9c2FJt+4U+2LQ9Jeh08edN/GoG1PSkq/SDVp/2y271RFHKhOferVU3zl3SUato+UEp21cwE206ea5fbFDW+3LniHEZzvRqfWjyQB+DffnuPN+7Vjr0nHdRs3Iw2iUlIOgcNa5VpfltCwXAIp8YMQKSWhjITS7A5/7a1730+afK/uQgd9wbMfu9rAD/nN78xie2FfHJKOoozxOG1PSkrKYHLQOvX8VJm//NGL/Nz/dTUay+H4aKqCEFEPVBzYxPpk6gqt1ccv3dqPTh2kVu2kU+vt3yWgCPDDkNtLrWOvScd1GzcjDaJSUg6Jw1rlWi+EtxZbtLon4qy+cfhrzF7T5IM4YXyvtdW1jhfZ6boBQzm9b69h0Po2Bm17UlJSBpOD1qk//f7TfPnWEl+8vhSVqgkBRMN9M7qyRp8WG/Zjl24dN51aP5LE1BVsL8TUVE4P5dBU8URo0nHdxvWkQVRKyiFzGCUovUL422/P8ctvPGSp5VDJ6pwom31Jkw/ihPG91lZLKQHBUE7v+2sYtL6NQduelJSUweUgdeovffQi891sQ0ZVyBkquhqZHBm6ssYN73FLt46bTq23f3c36V9+UjRpM47DNvaSBlEpKU8wp4dz/MWPXeTyRLGvafKjdsHbjt3WVq92PCCalzWywxDCx3kNg9a3MWjbk5KS8t5ifelWrROZLPhBiNfuX+nWcdMpLwgT+3dTV5Jyvs36l58kTdqM47CNkAZRKSnvCfqdJh/kCeO7ra0ezmewvRBFYeBeQ0pKSsqTzGGUbh03nVIUgeMHSMD2wi37l49ie1M2Jw2iUlLeI/QzTT7oE8Z3I9BDuQz/5tsz3ZK+nRmkKekpKSkpx52DLt06bjo1vRoNFFYEmNrW/ctHtb0pG0mDqJSU9xj9SJPvxwWvHza1e2Engf7cO3MD/xpSUlJSnnQOqnTruOnUv/zGPf7Nt+fww5DTQ7k1Mxw3I9WkoycNolJSUvbMcZowvpVAH6fXkJKSkpKyN47TOf70cI4/+6FzvHm/xu2lFpq6ffbsqLc3JWL7MDclJSVlE2J3obyhsdJytr3voE0Yj3kSXkNKSkpKyuYct3P8cdvelDSISklJ2SefevUUk2WThu2z2LAJwrW9Rb02tZNlc2AmjPfyJLyGlJSUlJTNOW7n+OO2ve910iAqJSVlX8TuQudH87iB5NZii5maxWLDZqZmcWuxhRvIgZsw3suT8BpSUlJSUjbnuJ3jj9v2vtdJe6JSUlL2zXGcML6enV7DudEcV8aLrLZdPvfO3MAO/UtJSUlJ2chx06n9bu9xGVD7JJEGUSkpKY/FcZswvhmbvYblpsPNxRb3ltu8NV1PGpNLps7zU2U+9ergiG5KSkpKytYcN53ay/a+M1PnV9+c5p2ZKOBKterwSIOolJSUvnBcJoxvR/wavnh9kX/1+gPm6jZtx0+G9VpeyFLTYb5hc3W2zs984hKfeGr8qDc7JSUlJWUXHDed2ml7v3h9MRnYm2rV4ZMGUSkpKSk9vDNT5x/+3i3uLrcpmRqT44U11rhBKFlpOdxdbvMPv3iLsYIxkKt8vSuYHdcHBLmMOtCrrykpKSkpu+M4a9Xrd1f4wrUFmrZP0dT45DMTfOD8yFFv1p5Jg6gB4bikmFNSnnR+9c1p5uo2JVNjvGRu+Luq9ZOs9gAAL11JREFUiO7vbebqNr/2zemBESZYW9qx2LSpWx6OHw2bNDSVSlZnrGikZR4pO5LqUkrK4HIcteo3vjXDL3z5Ng+rHVxfIpEIBL/0Bw84PZzjL330Ij/28tSRbuNeSIOoI+ZJrWVNxTflOPKw2uGdmTptx2dyvLDtfUcKBrcWW7w9XedhtTMQ7+/e0o5ax8UNJGEoCWVkk+sHEssLWGm7aZlHypY8ibqUalLKk8Rx1Kq///mb/NOv3MHyQsJQoigCISCUkrYTcnOhxd/8jbe5X23zX37yypFs415Jg6gj5EmsZX0SxTflvcPV2eh9WzC1bafbQ7TKVzA1GrbH1dn6kV+Q9ZZ2GFo0vUJKiaYIdFUBIfCCsDt3RCIlA1nmkXK0PGm6lGpSypPIcdOq3/jWDP/0K3douwGqIsgbKop4tN2hlNh+SNsN+CdfvsPZ4fyxyEilQdQRcZxrWbfiSRPflPceHTcgCCX6DqIUoyuCUEo6bnDAW7YzvaUdHTfACySqEGS0R+MAM6qCS4gXSIJQUjK1gSnzSDl6njRdSjUp5UnluGnVL3z5NpYXoiqCnK5u+Lsiot93vADLC/nHX76dBlEpW3Mca1m340kT35T3JrmMitq9yNoNXhjN7shlNorCYdJb2jE8lGWx6RKEkqy+cZ66ripYbkDT9jA0g2rb5cs3l3j97sqxbOxN6R9Pki6lmpTyJHOctOr1uys8rHYIQ0ne2P7/m5pC2wm4t9Lm57/wLlND2YEuv02DqCPgONay7sSTJL4pR8Mg9Cw8d7JMydRZajpJ2c9WBKGkZftMFE2eO3m07+Xe0g7bD5NtF2Lj9oehRMqoN2qubhNKycOqxd/49bf5vstjaXnTe5QnTZdSTUo5aI5Ss46TVn3h2gKuH/VAKZtoUi/d9l0sN+Sff/Uuw/nMQJffpkHUEXDcall34kkT35TDZZB6Fk4P53h+qsx8w2al5Wx68RWz0nLIGxovnDr6FbLe0o4gjB2PNuIFIa4fEkqQRHXodBt75+o2n7+2kJY3vUd5knQp1aSUg2QQNOs4aVXT9iNN2qHyMNanbhwVfS/lQJffpkHUEXDcall34kkS35TDZRB7Fj716imuzta5u9wGbEYKxqZlQA3b5/xonh9/5dShbNd29JZ2ZHUFgeCRFEUEoVwTQAmi0r5QRiuEJysmYSjT8qb3KE+SLqWalHJQDJJmHRetKppapElSbnmfXn2KKWf1JDgc1PLbjQXzKQdOfMHjhVu/oXrxQhk13R1x38VWPEnim3J49PYsZFTBpfECU5Us4yWTqUqWS+MFMqpITprvzNQPZbuenyrzMx+/xPnRPG4gubXYYqZmsdiwmalZ3Fps4QaS86N5fuYTlwbiRB6XdrRsH1NTUOOMVI9oeUFICCCiAEoRAkVE4qQpgqIRCVav2UTKe4cnSZdSTUo5CAZNs46LVn3ymQkymlgzbmM9iT51UQUM5TOPfu6W3w6aPqWZqCPgONWy7obj1OCYMjgMcs/CJ54eZ6xo8GvfnObt6WhVO5TR+3aiaPLCqTI//srg1Gb3lna0HJ+CoeIFkQtfLF5RUBUFUIho/wZSdu1mNYyuCUVa3vTe5EnSpVSTUg6CQdSs46BVHzg/wunhHDcXWth+uMGdr1efYgxdpWhuDFEGTZ/SIOoIOE61rLvhSRLflMPhOPQsPD9V5vmp8kAYXuyG3tIOQ1PQVYHjRzXm8KiED7olCN0slKmrjBWN5HnS8qb3Jk+SLqWalNJvBlmzjoNW/aWPXuRv/sbbtN2AjhdgakpiMhFIuab4XBVwspLd9HkGTZ/SIOqIOC61rLvhSRLflMPhOPUsnB7OHYv3alzaEdfrAwgh8ONVvu79RDcVJYhW+6Yq2Q22s2l503uTJ0WXUk1K6TfHQbMGWat+7OUp7q20+adfuYPlhbSdAEURiO5iXpyFUgVMVrKMFjJbPtcg6VPaE3VEHJda1t3yqVdPMVk2adg+iw2bINzY1L7YsGnYPpNlc2DFN+VwSHsWDoZPPD3Oz/7oc/zAsxM8faLEiZJB3lDR1ChoEiIauJvLqAzlMpwbyVPJ6RueZ5D7XVIOjidJl1JNSuknqWY9Pv/VH7nC3/6xF3hqokDeUFFFpEtqtz9XVwXnxwqcGto8CxUzSPo0MJkoIcT3An8T+BCQBd4F/jnw81LKfb8LhRB/E/gfuz/+gJTy84+7rf3iONSy7pb1q+C3FluJa43XLZfIG9qxEN+UgyftWTg4NivtmFnt8CtvzrDccjhZMSkaetIDtZ60vCnivahJ8OToUqpJKf0k1az+8GMvT/FjL0/x+t0VvnBtgabtA5I3H9RYbDgMbbKo18ug6dNABFFCiD8B/BpgA58GqsCPAn8P+DDwp/f5vK8A/z3QArYvYj0ijkMt6255UsQ35eBJexYOnvWlHSttj89fWyAM5ZYBFKTlTfDe1iR4cnQp1aSUfpFqVn/5wPkRPnB+JPn5b/3mVT5/beHYld8eeRAlhCgB/wQIgI9LKf+w+/v/Dvj3wKeEED8ppfzlPT6vCfyfwBvAbeA/7OuG95lBrmXdC0+K+KYcLGnPwuHzpPS7HDSpJj3iSdClVJNS+kGqWQfLcdWnIw+igE8BY8AvxmIFIKW0u2UPXwD+CrAnwQL+X8B54CXgv+3TtqbskidBfFMOluN60jyupOVNuybVpCeQVJNSHpdUsw6O46pPgxBEfX/36+c2+duXgQ7wvUIIQ0rp7OYJhRDfD/yXwF+VUr4rxO4aAVNSUg6P43rSPM6k5U27ItWklJSUDaSadbAcR30ahCDqqe7Xm+v/IKX0hRB3geeAC8C1nZ5MCFEG/gXwFeB/2+9GCSHe3OJPT+/3OVNSUtZyHE+ax520vGlHUk1KSUnZlFSzDpbjpk+DEETF77T6Fn+Pf1/Z5fP9PDBMVMsud7pzSkrK0XLcTppPCml505akmpSSkrIlqWYdPMdFn/oSRAkh7gFn9/CQX5JS/tl+/O912/HjRM26/5mU8s7jPJeU8tUt/sebwCuP89wpKSkbOS4nzZTBJ9WklJSUgybVrJR+ZaJuE1nB7pbZnu/jVb2tcp/x72vbPaEQYhj4/xI1/f6jPWxLSkpKSsqTRapJKSkpKSkHSl+CKCnlJx/j4TeA14ArwJqabyGERuRm5AM7reKdAUaBTwLhFo27v9v9/V+VUv79x9jmlJSUlJQBJdWklJSUlJSDZhB6ov498NPADwL/at3fPgrkgC/vwgVpBfhnW/zto8Bl4LeJVhzf2ffWpqSkpKQ8yaSalJKSkpKyI4MQRP0q8D8BPymE+PmewYYm8Le791lTCiGEyBGt8nWklA8ApJQPgf9ks38ghPgXRIL1v0gpP38QLyIlJSUl5Ykg1aSUlJSUlB1RjnoDpJQN4D8FVOD3hBD/VAjxd4FvA99DJGifXvewDxBZy/7iIW5qSkpKSsoTTqpJKSkpKSm74ciDKAAp5W8AHyMaZPjjwH8BeMBfA34ytYVNSUlJSTksUk1KSUlJSdmJQSjnA0BK+VXgh3d5398Ddj3yXUr554E/v5/tSklJSUl575FqUkpKSkrKdgxEJiolJSUlJSUlJSUlJeW4kAZRKSkpKSkpKSkpKSkpe0Ckpd17Qwixks1mh5955pmj3pSUlJSUY8e1a9ewLKsqpRw56m15Ekg1KSUlJWX/PI4mpUHUHhFC3AVKwL09POzp7tfrfd+g40e6L9aS7o+1pPtjLU/i/jgHNKSU5496Q54EUk1K6ZIe0yeP9JgeDufYpyalQdQhIIR4E0BK+epRb8tRk+6LtaT7Yy3p/lhLuj9SDoL0ffXkkR7TJ4/0mA4+aU9USkpKSkpKSkpKSkrKHkiDqJSUlJSUlJSUlJSUlD2QBlEpKSkpKSkpKSkpKSl7IA2iUlJSUlJSUlJSUlJS9kAaRKWkpKSkpKSkpKSkpOyB1J0vJSUlJSUlJSUlJSVlD6SZqJSUlJSUlJSUlJSUlD2QBlEpKSkpKSkpKSkpKSl7IA2iUlJSUlJSUlJSUlJS9kAaRKWkpKSkpKSkpKSkpOyBNIhKSUlJSUlJSUlJSUnZA2kQlZKSkpKSkpKSkpKSsgfSIColJSUlJSUlJSUlJWUPpEHUASKEOCWE+OdCiFkhhCOEuCeE+PtCiKGj3rZ+I4QYEUL8J0KIzwghbgkhLCFEXQjx+0KI/7sQQll3/3NCCLnN7ZeP6rX0i+7x3ur1zW/xmO8VQnxWCFHt7sO3hBD/lRBCPezt7ydCiD+/w/GWQoig5/5PxPtDCPEpIcTPCyG+IoRodLf9X+7wmD2/B4QQPyKE+L3uZ64lhPgDIcSf6/8rShlE+qU1Qojh7uPudZ9ntvu8pw5q21M2px/HVAjxA0KI/1kI8QUhxEr3/PP7B7ndKdvzuMdVCJEXQvy0EOL/J4S4LoRoCyGaQog/FEL8dSFE5qBfQ8oj0mG7B4QQ4iLwNWAc+DfAdeADwCeAG8CHpZQrR7eF/UUI8ZeBf8T/v70zj7OjqPb49xcwCQQMgSi7hC1sKqsLS8iETVAhrAKKEvTx1MciiCtPHkHAHUFBBRQIoigCSkQIIIQAsiOERfYlGCCAkARIICEh5/1x6iadnu47987cOze5c76fT316blV11amumjp9qmuBacCNwL+BVYF9gcHA5cABlhqcpGHAM8D9wBUFST5kZpc1XfAmImkKsBJwRkHwLDP7SS7+aPw5zQEuAaYDewIbAZeZ2QFNFLepSNoC2LskeASwE3CVmX0yxR9GG7QPSZOBzYFZwHPAxsDvzeyQkvh1twFJRwJnAq+me94G9gfWAk4zs681tlTBkkSjdI2kVVI6w4GJwN14ex0NvAxsa2ZPN6MMweI0sE6vwOtvDvAk8H7gVjPboTmSB9VoRL1K2h2YgOuGG/F6HQLsBayW0t/ZzOY0qRhBFjML1wQHXAsYcFTO/6fJ/+xWy9jg8u6Ev+z1y/mvhhtUBuyX8R+W/Ma1WvYmPpMpwJQa474bf1GZC2yT8R+Id4oGHNTqMjXpOd2eyrdXu7UPXDluCAjoSGX6XaPaQHpOc3ADaljGfwiuXA1/+W35swjXtDbWEF0DnJPin5bzPzr5X9PqsvYV18A63RbYDFgm06f+o9Xl66uuEfUKbAF8Buif818R+GdK57hWl7WvuJYL0I4OWD815GcKjIoV8VHp2cCgVsvaS8/j+PQ8zsz4tcVLchflrseI+nx6HhcWhO2Uwm5qdZma8Iw+kMr2HLBMO7ePGoyoutsA8N3kf1I96YVrD9coXQOsALyZ4q+YC+uX+jID1mt1mdvdNev9IYyo9qzXXDqfTnlc2ery9hUXa6Kaw6h0vc7MFmQDzOwN4FZgeeCjvS1Yi5iXrvMLwtaQ9EVJx6frB3tTsF5ggKRDUvm+ImlUydqWndL1moKwm/EXnO0kDWiapK3hv9P1PDN7pyC83dtHlu60gWr3TMjFCdqPRumajwLL4VO93silswAfQc/mFzSPeH9oT3qjXqu9awVNIIyo5rBRuj5eEv5Eug7vBVlaiqRlgc+ln0UversCZwOnpuv9km6U9L5eErHZrAZchJfvDHytwROSRubilbYZM5uPj14tC6zXNEl7GUnLAYcA7wC/KYnW7u0jS3faQLV7puEjm2tJWr6xogZLCI3SNaGzlhyiLtqT3qjXz6dr0btW0ATCiGoOg9P1tZLwiv9KzRel5fwAX8x6tZldm/F/EzgZ2BpfvzEEGIkvlOwAbpA0qHdFbTgXADvjhtQgfOraOfi0igmSNs/E7Ytt5lN4ea4xs6m5sL7QPvJ0pw3Ues/gkvBg6aZR/UZf7H+WVKIu2pOm1mvaYGh3YDJwfnfSCOonjKigaUg6GjgO34Hms9kwM3vZzP7PzO41s5nJ3QzsBtwJbAD8V68L3UDM7CQzm2hmL5nZm2b2kJl9CV9EuhwwtrUStpzKVL5z8gF9oX0EQRAEQU+RtC8+0+VFfAOvedXvCBpFGFHNoavR34r/zOaL0hrSqMjPgIeBUWY2vZb70rSlytSuHZskXqs5O12z5etTbUbSZsB2+IYSV9d6X5u3j+60gVrvKRv9DJZuGtVv9Kn+Zwkn6qI9aUq9Stob+CO+s2uHxTEEvUoYUc3hsXQtm9u6YbqWzY1dqpF0DH5uzUO4AVV4sGwV/pOu7TZdq0JR+UrbTFpXti6+WLRdOsiuNpSoRru2j+60gWr3rI4/o+fM7M3GihosITRK1/RpnbWEEXXRnjS8XiUdAFwKvASMNLPHurglaDBhRDWHG9N1N0mLPWNJKwLb42s+7uhtwZqNpG8Cp+PzckeZ2cvdSKayO027GAx5iso3MV13L4i/I75rz21mNreZgvUGkgbi0zvfAc7rRhLt2j660waq3bNHLk7QfjRK19wBvAVsn+7LptMPn0abzS9oHn32/aHNaWi9SvoM8AfgBdyAeqKLW4ImEEZUEzCzp4Dr8A0EjsgFn4SPDl9kZrN7WbSmIukEfCOJf+InZr9SJe5W+Y4k+e8MHJt+/q4pgvYCkjYp2vhA0jDgrPQzW77LgFeAgyRtk4k/EDgl/fxVc6TtdQ7AN4qYULChBND+7aOE7rSBC/DDeY9MbatyzxD8fDZYNH00aDO6o2skbSxp41w6s/BdRAfRea3mkSn9a2OqUPNpVJ0GSxaNrFdJhwK/Bf4N7Bj/l61D5gd0BQ1G0vrAbcB7gfHAI8BH8LMCHge2M7NXWydhY0n/1OPwrwtnUrwGY4qZjUvxJ+Gfr2/D18UAfJBFZ9qcYGan5BNYWpA0Ft9U42bgWeAN/LC9TwAD8XVA+5jZ25l79sZfpOfgc5ynA3vhW6NeBnzK2uAfVtItwA7AXmZ2ZUmcSbRB+0h1unf6uRrwMfwL2i3J7xUz+1oufl1tQNJRwM+BV4FLgLeB/YG1gNOy6QftR726RpIBmJly6ayS0hmOf728C9gEGI2vt9guvQgGTaaBdboDizbgWQHYD6/LyhlymNmYZpUjWJxG1KukUcD1+EeQ84GigciZZnZGc0oRLEarT/ttZwesjY8UT8NfbJ7Fd1AZ0mrZmlDWsfhJ2dXcpEz8LwB/A6bgJ3XPxUdVLgFGtLo8DXgeI/FP7Y/iC0Xn4Wt5/o6fm6WS+7bHDawZ+PSaB/EvL8u0ukwNei6bpLYwtVqZ2qV91PB/MaURbQDYE7gJN9ZnA3cDh7a6/OF6x9WjayptrySdlfENgZ5N6UzDX9TWanUZ+5prRJ0CY7rSy60uZ19zPa3XWuq0SK+Ea46LL1FBEARBEARBEAR1EGuigiAIgiAIgiAI6iCMqCAIgiAIgiAIgjoIIyoIgiAIgiAIgqAOwogKgiAIgiAIgiCogzCigiAIgiAIgiAI6iCMqCAIgiAIgiAIgjoIIyoIgiAIgiAIgqAOwogKgiAIgiAIgiCogzCigiAIgiAIgiAI6iCMqCAIgiAIgiAIgjoIIyoIgiAIgiAIgqAOwogKllgkjZNkkoa1WpYlEUkHS7pP0hvpOZ3Rapl6G0ljUtnHNCi9aHNBsBQhaVj6nx3Xaln6ApJ2k3SbpJnpuV/Rapl6G0kdqexjG5Te2JReRyPSC3qPMKKCPoGkSZKs1XI0CknbAr8HVgR+BZwEXNNSoZYCQlkFQdBbtNugTCrHeGBd4Hxc7/yxlTItDTR6sC9Ycli21QIEQdAtPgEI+JyZ3dZqYdqIbwM/AJ5vtSBBEARLGLsAA4HjzOziVgvTRpyFG6P/brUgQX2EERUESydrpOsLLZWizTCzacC0VssRBEGwBBJ6pwmY2SvAK62WI6ifmM4XdCI7x1zScEmXSHpZ0oLKNChJW0v6maT7JU2XNEfSE5JOkzSkIM2Fn7MljUrT696Q9LqkqyRtUod8m0t6Pt27ay1lAUam35ZxkzLxpiT3bkk/TX/Pq8x5lrSGpP+TdKukFyW9LekFSRdL2rSLZzhM0h8lvZKe0z2SPllwT39JR0u6V9IMSW8mOcZL2iX7HIHD0m3PZMozLJPW1pIuT/U2V9Kzkn4pafWCfCtTTtaTdJSkByS9VXk+mfB1JR0p6eFUjimSjpekFO8ASXdJmp3yPUvSciX1snFKd2p6li+lZ7lRSfwNJF2ansts+Zz8TxTFLUPSFODE9PPGbFsoeBbZZ5mty/UlXSbp1dR+r5P0/hTvPZLOlTQtPZ+7JY0qkWVZSf8j6Y7Ujt+Ur287UlL0y0HQQyT1k+sok/TnSl+kzJRe+brSf6b/vxdS3z8gxdtJrqdeT/3ORZJWqSN/Aw5NP7P99JRMnEnJr79cvzyW+utxKXywpK9LmijpudRX/kfSX+VTugvzTekOzfRHcyX9S9JhBfEl6dDUp/4n9V1TJV0r6cAUpyOV56R0W7b/7MiktaGk38r1c0VH/lbShgX5Zuvh05LulDSr8nyaUU+S1pLrpafTM3k1PcsPlcRfVdJ5cv30lqTJkg4tiluGXI9ekH5eoMXfQYbly5q7t1KXq0o6P8lR0X8jUpxBkn4s1/GVej6gijwHS7pRvqZtjqRHJH2n8jyD+ogvUUE11gfuBB7H198sB7yewg4H9gFuAq7HDfKtga8Ce0j6iJm9UZDmJ4HRwATgbGBT4OPAhyRtmkZkSpG0M/BnYDawo5lN7qIMM/GOfwywDouUAMCUXNz+wERgZeC6VNZnUtiOwLeAG4HLgVnAhsD+wF6Stjez+wvyXwe4C3gauCilfSAwXtIuZnZjJu444GDgIeC3wFv4yN8OwO74c56cyrA3sDnws1TGSlmRG2iX49P9LgOexevmy8BoSTuYWaVcWX4GjACuAq4G3smF/wToAK5Mz2cv4FSgv6Tp+DS4K4BbgF2BI4BlUr4LkbQ7XofvSmk9CawF7At8QtIoM7s3E39D4HZgFbzdTAY2SHlNKChHGWfgz20kcCGd678rhuH/D4/gdTUM/x+YlF5orsHbzCV4PR8ETJA03MwWTtOQVCn3x4DHgIuBOcAo4EzgI8Bn65QtCIKEpIG4ztoX+AVwtJktyEU7CtgD70cmAbsBxwIrSxqPT6+6CjgX2A44BBia7qmFqv10jsuBD+H92RXAy8l/E7yPvTnJMgN4H9737iFpTzMrWgu7EnAr8DauAwYABwDnS1pgZhdm4p6KT2N+BvgT8BqwepLnALw/m5LK00Hn/nMKQDJErsfX6f4VeBjYGH9uo5O+u7tA1uNwfXElrl8H58IbUk+StsL11srAtbgOGorX0T8k7WNmV2fiDwVuA9YD/pHc6vh7y3UF5ShjHF7no/H1ZJMzYTNruH8lvC7fAP7AIt1ybdI75yS/v+E69WDgEklTzeyObEKSzscHYJ/D29xM4KPAycDOknY1s/l1lC0ws3DhFnP4y6El972SOOsAyxT4fyHd982c/5jkPx/YORf2/RT2jZz/uOQ/LP0+BFcKDwPr1FmmSd7cS8OnpLyuBwYVhL8XWLHAf3PcoJpQ5RmemAv7WPK/OuM3GFgA3FPyXFep9mwy/isAr+IG0Ihc2DfTPdeVpPU8sG5B3pXwKcCaGf+V8CkIs4H/AJtkwgakepoLvDfjPwR/EXgF2DSXz/vTs7w3539dyv8rOf/RmWc8psZ2MDbF7ygJ7/Rcc3X5v7n4JyT/6bhy7ZcJ+2wKO71EhjOzdY0bnOelsNH1tO9w4fqqy/x/jku/V8ZfeBeQ00MpvPL/91pBn/Wv1He+CozMhPUD/p7u26IO2Qr76Uz4pBT+ADC0IHxwif9a+JS6RwrCKn3Vb3L9y6a4/n04F/9V/KV6+YK0huZ+F/af+IDdIynsM7mwA5P/o7n+sZLWbGDLZtYT/sHgSXywamQunzVw3TcNGJDxP7ek/94GmJfCxtbYDsZQRU9Vea6VuizTLdNx43NgJmxECvtLiQx/BpYryf8rzfgfbWcX00aCarzE4l9uFmJmz5pZ/ksF+I49r+OGQhF/NLMbcn7npuuHywSR9C3868ydwPZm9mw1wXvAcWY2O+9pZi9bwZc1869PE4FR6QtDnmeBU3L3XIsvIM2W13BFNBdX/vl8Xq1R/tH4S8QlZnZLLuw03BDaVdL7Cu79kRV/oapwspkt3HDBzGbiI47LA78ys0cyYXPxEcz++Ghqhc/hxteJZvZwNnEzewj4NbCl0hRJSWvho5TP4Itvs/HH419Ce4sp+Ne2LJUR3QHA123x0e6L8ZeWLSoe8ql6RwEvAsdm/4fS38eRXkQaLHsQtD2S1sFH7T8MfNbMflgl+s9L+qx+wFVmdlMmbAHwu/Rz84YLDidYwSwMM3utxP85/AvTxiV9+ZvAV3P9y8P4s9lE0gq5+PPoPPOAorxL2A7/6nS7mf0+l8YluFG7ET6rIs+5ZnZflbQbUU+fwGfWnJmNn+55AfgRsBqwMyycLfAZ/OvP2Fz8e/CvnL3Fm5TrliG44TMnI98tuK7aIpfOV9I9nzezt3JhJ+MGaeidOonpfEE17k8dVidSJ/NF/LPypviIWdYoX7MkzXsK/Kama6e1VInT8U/ulwOHZDuMjDzH4C/nWa6wrqf7ZZmDjwgWIl+D8yV8JGoonf9/htJ5U4LJJcbmVGDhnHYze13SlcCewGRJl+PT4u40szfrKMNW6ToxH2Bm8yXdjI/cbknnnYDu6iLtorqrLDD+Z0FYxeBaK+NXKfPmKj5jY3i6boJ/ydoy/f5HyXOcRFrv1gsU1WWl/I/njWwze0fSSyxe/uG4kfsE8B35crI8b7G44RkEQddshE/7HQTsUTBYl6fH/Zl8y+phuXiTzGxSF3nnKe17JW2PvwBvi8+I6J+Lsiad+/InzOx1OpPVtbPS37/HB3YelvQnfGDqdjN7rQ75S/VOxn8HvD+/ORfWm3pnnRK9U1mztQk+lX1jfHDwlpLnMIlF692aTTXdMsjMni6453l8WjgAkpbHjcpXgGNK9M5cQu/UTRhRQTVerBJ2Cb4e5Gl8nu+L+D8hwDH4yHwRM/Me6eUefDpTETum69+KDKhMnuvk/Kaw+PzjrnjZzL9t55H0FXxNzQx8usC/8REiY9G896IyzyzJaz6dN3Y5EJ9y92kWfQGcI+ky4Gtm9lINZRicrmU7zFX8VyoIq1bf4NMq8syvISz7ha6y4PfwLvKqjJRWylNW9q5kbiSdyphpu2UvHPMpLv+GLNrkooj8SHEQBNWpDFBMBu6tHhVoTH82huJBnEk15J+lsB+TtA/+xWkOrneewqe/LWDR+qR69Q4srmuPxfX4Yfi6328B8yVdjc/MeLIG+ZcWvVO64UJiqdA7ifldhGXf74fgM13eQ3W9E9RJGFFBNcoMim1wA+p6fMRvfiasH/CNBsuxNz5N8DxJ7zKzX3cS1GxYA/IpK++y+Cf9F4GtzLfBzoYX7pJUd+b+iX0sMFbS2rjxOAZfCzYMn+vcFZVOdbWS8NVz8RYToUZRe0Il383NrPSrX0H8VUvCy8q5pFIpz1/MbN+WShIE7cWV+EYt3wNuSIvka50G3S3MrKNB6ZT1vSfj64C3yU5pA5B0Dg34Cp++rp8BnCHpvfgXo4Nwg2MzSZuVzUjJsLTondFm9tc64reb3rnPzLaqGjOoi1gTFXSHDdL1r9Z5J5cP47v4NZKpuEHxGHCOpCO6kcY7AJLKvnZVYyg+gnZbgQG1AoumMjQMM5ua5pZ/DF8Qu0PZtq05KnPLO/IByRisGGK1jNQ2g8puQbUYhLCoPDuU1F1HnflXpuN1px00gkdJOyKVrKELgqCbmNn38S8rW+K7Zpa9BPcWPe1vNsA3gsgbUP0oXl/UI9La3z+b2afwKXjr4xv+dEWp3klUjnpYWvTOo/hMky0kDS4I76gz/5bqHTObhW/GsZmklVshQ7sSRlTQHaaka0fWM41i/aIZGSbjZSTwIHCWpOPqTKIyIlm0CLcrXsY71K2zC3LTS/DPcCOrR8jPGPpAQdAgfIrBfHxEsiuuwHfsOVjSR3NhxwDrAtdbZsvtXuYC3Ig4UVKnjUTkZ7t0VH6nBdR/x+U+Mhd3NPWPxPakHfSYNOhwJj4y+3MVnKMlaXUVnD0WBEHXmNkZ+LEKmwE3SVqj+h1Npaf9zRRgw2wZ5POHx+JrkXuEpAFpzVXe/1341Ehw3dcVt+KDnDtI2j+X1v648fI4vsFEKxiPT4U8QtLHiyJI2jatHcLM5uFrxVYkt7FEmolT7wYMLdU7iZ/i6+nOl7RSPlDSkLQNfFAHMZ0v6A53453mvpJuwzvGVfFzGR6jSaeZm9l/5IeXXgv8RNJAMzu1xttvwKcn/DnN9X4LeNbMLqoh3wWSfo7PFX8wnU3RHx9dWxk/26LwUNU6WBO4T9KD+OYWU4F34+dqrYbvUFR07lZe1lmSPg9cir9AXIqv39oaP1/jRXxDkJZgZq8mpfoX4A5JN+AjZAasjS8AXgUYmLntCHzB+BmSdgPux0do98Gn8OxZhwg34usJvi8/JHdGkuuUqnc1lpPxNXRfAvaUNBFfCPxefK3U9sD/4htrBEFQJ2Z2tqQ5+JEBN0vaqUUDRzcAXwd+nTYLegOYaWZnVb9tIafj21vfl+6fh/cPm1J/31fEcvgZSU/imzQ8i/e9u+KbDPw1/xWsCDMz+SG0f8fPKBqPf83ZCJ+O/wbwOet8VlevYGbzJO2Lvztcld5bJuMG4tr4mVjr4YNbFaPxeHy3vmOS4VQ5J+pAfPOJveoQ4faU7jFpRkllTdWZdW7g0W3M7HxJWwP/AzwlqbJL8Mr4IOWO+CDnl3pDnnYhjKigbtLOMHvhW3d/HDgafwn8TfJr2sufmU2XH7g7ATglGVIn1HDrb/CNJw7C12wti+9C1KURlTgBPwvpv3Aj5DVcYXyHkm3g62QKvuCzAzfIhuJflB7Djbc/1pqQmY1Po4vH49MBB+Od9tn4NuVNMXLrkO8GSR8EvpbkG4F/ZXsBn0JyeS7+E+mr2g+AXfBn9ACunN9DHS8SZvZIUvZfw5VJxVjrNSMqKfS98bVuY3BDeQW8fT2Dt7Xe3EI3CNoOMxsnaS5+NEbFkCrayayZMlybZk0cjs8E6I8bKjUZUWZ2TirDMfhucG/hu7YeBuxHz42o2fhmRqPwbcr3xg2ep/CveefXmpCZ3Sk/cPc7eD+9J74b3B9wvfNYD2XtEWb2gKTNga/ife5h+IDaNHw64om4vJX4ryQ9+j28LNvg+vjLuL6u2YgysxmS9kt5jMFnmIBvx94rRlSS4whJE3BDaRd8mcJ03Jj6MYu2hw9qROXrGYMgCIIgCIIgCII8sSYqCIIgCIIgCIKgDsKICoIgCIIgCIIgqIMwooIgCIIgCIIgCOogjKggCIIgCIIgCII6CCMqCIIgCIIgCIKgDsKICoIgCIIgCIIgqIMwooI+j6QLJb0saVDXsYPeRtK+kiydDxYEQdDWhE5aspH0VUnzJG3calmC1hJGVNCnSYcDfhb4gZnNXgLkWUbSsZIekPSWpOmSrpa0XTfTO1TSXZJmSXpN0iRJn2xk/pKWk3SSpMckzUnK/0+SNimI25EMoq7c2pnb/gLcC/xUUvRZQRC0LaGTep5/PTopc89+SZbXUj7/kvRtSf0Lov8KPxz9J3UVPmg74rDdoE8j6Trgw8DqZvZWi2UR8Cdgf/xk9CuBlYEDgYHAfmY2vo70fgIcBzwHXAb0Bw5KaR5lZmf1NH9JA4AbgO2Be4CJwNrAAcDbwE5mdmcm/jD8xPYiPgDsCzxkZh/I5fMp4BLgM2Z2ca3PIAiCYGkidFLP8q9XJ6V7vgd8G5gFXA5MB0YA26S09jCzebl7vgH8ENjezG6r9RkEbYaZhQvXJx0wHFgAnNtqWZI8BwMG3AoMzPh/CJgLvAysWGNa26W0ngSGZPyHAa8Cc4BhPc0fVzwGXAr0y/iPTv7/yvp3IfMf0j1HF4QNBGYA/2h1PYULFy5cM1zopN7XScBWyX8GsF7GX/gXJwO+WlCeNYB3gN+1up7Ctc7F1JigL/N5vKO8JOuZmXI2VtK2kq5Pn/jfkHStpG2aJM+X0/U7Zjan4mlmdycZ34OPyNXCl9L1VDObkUlrCvALYABwWE/yT6OElXy+YWYLMveMB24BNgVGdiWspKHAPsBbwG/z4UmeK4DtYx56EARtSuikHuTfTZ20d7r+xsyezsQ34Pj084h8YczsBeBmYH9J7y4rdNDehBEV9GV2wUeS7igJ/wgwCR/x+gUwAdgZuEXSiEYKImkgPlL3Jt7R55mQrjvVmGQl3jW1pNXN/NcH3gc8bmbP9FDmQ3EleqmZzSyJc2u67lJDekEQBEsboZN6ln93dNJq6fp0Li7J2JsBrCdp3YL0bsX11o4FYUEfYNlWCxAErUC+69EWwCNWvnh3d3LztCWNxr+InC9po8pIVxdrfcoYl0bhwDv/ZYCnzWx+Qdwn0nV4V4mmsq0JzDKzaTWm1Z38N0rXx0tEqVlm4PB0PadKnLvTdUfgrCrxgiAIlipCJ3VKq7d00ivp2slIkrQSMCSTdt4wy+qkv5XkGbQxYUQFfZU18Q66qEOv8CTwy6yHmY2XdBM+HWAEcFMKGgacWKcMk4Ap6e/B6fpaSdyK/0o1pNudtHrrnk5IGokrqIes+gLdF9P1fdXSC4IgWAoJndQanXQVvo7qcEm/rBiRaWrgqZl4Q+hM6KQ+ThhRQV9llXSdUSXOLdk51Rkm4QprS5LCMrNJ+Fz2oH7+O13P7SLe9HQd2kRZgiAIWkHopBZgZrdKOg/4AvCApOzufB8EHgU2xjf8yBM6qY8Ta6KCvkpl69iBVeK8VOJfGX0aXBLeHSojZGVpVvxnNimt3rpnMSStDOyH18dFZfESy6VrS7f9DYIgaAKhk1qnkw4Hvohvo/6p9PfrQAfwVIrzckF6oZP6OPElKuirVDrEVarEWbXEv7IQdeGUgQbMP38KX1C8nqRlC+aAb5iuZXO9F2JmsyU9D6wpafWCOehFaXUn/8fStWxOfC0yVzaUuLDKhhIVKnVVpMyCIAiWZkIntUgnpZ34zqVgNoSkD+Bfoe4tSC90Uh8njKigrzINP3F8oypxdpDUr2D6REe63pfxG0YP5p+b2RxJt+FTCEYAN+bi7pGuE2tMeyJ+6v3uwAVdpdXN/J8C/g0Ml7RuwW5Itchc2VCiq6l84FMqACbXEDcIgmBpInTSkqGTFiKpA1/vdKWZFa2zCp3U12n1QVXhwrXK4SemG7BBzr8j+RtwZC6scmDfE9R4iGwd8tRysOC7c/esjnfkg3P+zTrYMJ9/tw/bxRWjAQ/W+HxOSvE/2eq2Ey5cuHCNdqGTWqOT8mkkv3WSrHOBD5Y8nwtTmu9vddsJ1xrXcgHChWuVy3TQR+T8KwprQupArwK+B/wJmI/Pfx7RBHmUOn4DHgF+BJwHzEr5ji64Z1yKP6Yg7LQUNhU4HT9X5JUiRdyD/AckBWf4dq8/AC4G5gGzgY9UKe9F6b6janw+t+OLrpdvddsJFy5cuEa70EkNyb9unZTyuAs4Oz3XP6S4bwMHljybfsDzwKOtbjfhWudaLkC4cK1yQH98oe6dOf+KwhoLbAtcjy8yfQO4DvhQE2VaFjgWeDApxhnA1cB2JfFLFVYKH5MUyewk/01U+ZJTb/7pnuWB7+IjoXPxKSmXAptWuWdISv9NYKUansvwVM4zWt1uwoULF64ZLnRSz/NP99Slk/C1ubfiX8Texo28C4FNquSxWyrnMa1uN+Fa55QaQxD0SSR9Gx952srM7kt+Hfj865PMbGzLhAsWIuk04EhcqXU6WT4IgqAdCJ20dJC2Qh8JrG/F66WCPkBscR70dU7HF6J+t9WCBMVIWh34MnBmGFBBELQ5oZOWcCRtCewDjA0Dqm8TRlTQpzGzOfiOQfdIGtRqeYJChgE/BE5psRxBEARNJXTSUsFqwAn4GqqgDxNbnAd9HjO7Gbi51XIExZjZ7fimEkEQBG1P6KQlGzObgG/yEfRxYk1UEARBEARBEARBHcR0viAIgiAIgiAIgjoIIyoIgiAIgiAIgqAOwogKgiAIgiAIgiCogzCigiAIgiAIgiAI6iCMqCAIgiAIgiAIgjoIIyoIgiAIgiAIgqAOwogKgiAIgiAIgiCogzCigiAIgiAIgiAI6iCMqCAIgiAIgiAIgjoIIyoIgiAIgiAIgqAOwogKgiAIgiAIgiCogzCigiAIgiAIgiAI6iCMqCAIgiAIgiAIgjr4f4ybqRpSqiJ/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 284,
       "width": 424
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 284,
       "width": 424
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cph.check_assumptions(rossi, p_value_threshold=0.05, show_plots=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, you can use the proportional hazard test outside of `check_assumptions`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>time_transform</th>\n",
       "      <td>rank</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>null_distribution</th>\n",
       "      <td>chi squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degrees_of_freedom</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>untransformed variables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_name</th>\n",
       "      <td>proportional_hazard_test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_statistic</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>11.453</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fin</th>\n",
       "      <td>0.015</td>\n",
       "      <td>0.902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mar</th>\n",
       "      <td>0.709</td>\n",
       "      <td>0.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paro</th>\n",
       "      <td>0.134</td>\n",
       "      <td>0.714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prio</th>\n",
       "      <td>0.019</td>\n",
       "      <td>0.891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race</th>\n",
       "      <td>1.426</td>\n",
       "      <td>0.232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wexp</th>\n",
       "      <td>7.315</td>\n",
       "      <td>0.007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lifelines.statistics import proportional_hazard_test\n",
    "\n",
    "results = proportional_hazard_test(cph, rossi, time_transform='rank')\n",
    "results.print_summary(decimals=3, model=\"untransformed variables\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Stratification\n",
    "\n",
    "\n",
    "In the advice above, we can see that `wexp` has small cardinality, so we can easily fix that by specifying it in the `strata`. What does the `strata` do? Let's go back to the proportional hazard assumption.\n",
    "\n",
    "In the introduction, we said that the proportional hazard assumption was that \n",
    "\n",
    "$$ h_i(t) = a_i h(t)$$\n",
    "\n",
    "In a simple case, it may be that there are two subgroups that have _very_ different baseline hazards. That is, we can split the dataset into subsamples based on some variable (we call this the stratifying variable), run the Cox model on all subsamples, and compare their baseline hazards. If these baseline hazards are _very_ different, then clearly the formula above is wrong - the $h(t)$ is some weighted average of the subgroups' baseline hazards. This ill fitting average baseline can cause $a_i$ to have time-dependent influence. A better model might be:\n",
    "\n",
    "$$ h_{i |i\\in G}(t) = a_i h_G(t)$$\n",
    "\n",
    "where now we have a unique baseline hazard _per_ subgroup $G$. Because of the way the Cox model is designed, inference of the coefficients is identical (expect now there are more baseline hazards, and no variation of the stratifying variable within a subgroup $G$). \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>lifelines.CoxPHFitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration col</th>\n",
       "      <td>'week'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event col</th>\n",
       "      <td>'arrest'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>strata</th>\n",
       "      <td>[wexp]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baseline estimation</th>\n",
       "      <td>breslow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of observations</th>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of events observed</th>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partial log-likelihood</th>\n",
       "      <td>-580.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2020-07-26 22:15:41 UTC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>wexp in strata</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef</th>\n",
       "      <th>exp(coef)</th>\n",
       "      <th>se(coef)</th>\n",
       "      <th>coef lower 95%</th>\n",
       "      <th>coef upper 95%</th>\n",
       "      <th>exp(coef) lower 95%</th>\n",
       "      <th>exp(coef) upper 95%</th>\n",
       "      <th>z</th>\n",
       "      <th>p</th>\n",
       "      <th>-log2(p)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>covariate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fin</th>\n",
       "      <td>-0.38</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.19</td>\n",
       "      <td>-0.76</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.99</td>\n",
       "      <td>-1.99</td>\n",
       "      <td>0.05</td>\n",
       "      <td>4.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>-0.06</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.99</td>\n",
       "      <td>-2.64</td>\n",
       "      <td>0.01</td>\n",
       "      <td>6.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race</th>\n",
       "      <td>0.31</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.31</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.74</td>\n",
       "      <td>2.49</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.32</td>\n",
       "      <td>1.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mar</th>\n",
       "      <td>-0.45</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.38</td>\n",
       "      <td>-1.20</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.34</td>\n",
       "      <td>-1.19</td>\n",
       "      <td>0.23</td>\n",
       "      <td>2.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paro</th>\n",
       "      <td>-0.08</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.20</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.35</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prio</th>\n",
       "      <td>0.09</td>\n",
       "      <td>1.09</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.15</td>\n",
       "      <td>1.04</td>\n",
       "      <td>1.16</td>\n",
       "      <td>3.16</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>9.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><div>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Concordance</th>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partial AIC</th>\n",
       "      <td>1173.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood ratio test</th>\n",
       "      <td>23.77 on 6 df</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-log2(p) of ll-ratio test</th>\n",
       "      <td>10.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "\\begin{tabular}{lrrrrrrrrrr}\n",
       "\\toprule\n",
       "{} &  coef &  exp(coef) &  se(coef) &  coef lower 95\\% &  coef upper 95\\% &  exp(coef) lower 95\\% &  exp(coef) upper 95\\% &     z &    p &  -log2(p) \\\\\n",
       "covariate &       &            &           &                 &                 &                      &                      &       &      &           \\\\\n",
       "\\midrule\n",
       "fin       & -0.38 &       0.68 &      0.19 &           -0.76 &           -0.01 &                 0.47 &                 0.99 & -1.99 & 0.05 &      4.42 \\\\\n",
       "age       & -0.06 &       0.94 &      0.02 &           -0.10 &           -0.01 &                 0.90 &                 0.99 & -2.64 & 0.01 &      6.91 \\\\\n",
       "race      &  0.31 &       1.36 &      0.31 &           -0.30 &            0.91 &                 0.74 &                 2.49 &  1.00 & 0.32 &      1.65 \\\\\n",
       "mar       & -0.45 &       0.64 &      0.38 &           -1.20 &            0.29 &                 0.30 &                 1.34 & -1.19 & 0.23 &      2.09 \\\\\n",
       "paro      & -0.08 &       0.92 &      0.20 &           -0.47 &            0.30 &                 0.63 &                 1.35 & -0.42 & 0.67 &      0.57 \\\\\n",
       "prio      &  0.09 &       1.09 &      0.03 &            0.03 &            0.15 &                 1.04 &                 1.16 &  3.16 & 0.00 &      9.33 \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.CoxPHFitter: fitted with 432 total observations, 318 right-censored observations>\n",
       "             duration col = 'week'\n",
       "                event col = 'arrest'\n",
       "                   strata = ['wexp']\n",
       "      baseline estimation = breslow\n",
       "   number of observations = 432\n",
       "number of events observed = 114\n",
       "   partial log-likelihood = -580.89\n",
       "         time fit was run = 2020-07-26 22:15:41 UTC\n",
       "                    model = wexp in strata\n",
       "\n",
       "---\n",
       "            coef  exp(coef)   se(coef)   coef lower 95%   coef upper 95%  exp(coef) lower 95%  exp(coef) upper 95%\n",
       "covariate                                                                                                         \n",
       "fin        -0.38       0.68       0.19            -0.76            -0.01                 0.47                 0.99\n",
       "age        -0.06       0.94       0.02            -0.10            -0.01                 0.90                 0.99\n",
       "race        0.31       1.36       0.31            -0.30             0.91                 0.74                 2.49\n",
       "mar        -0.45       0.64       0.38            -1.20             0.29                 0.30                 1.34\n",
       "paro       -0.08       0.92       0.20            -0.47             0.30                 0.63                 1.35\n",
       "prio        0.09       1.09       0.03             0.03             0.15                 1.04                 1.16\n",
       "              z      p   -log2(p)\n",
       "covariate                        \n",
       "fin       -1.99   0.05       4.42\n",
       "age       -2.64   0.01       6.91\n",
       "race       1.00   0.32       1.65\n",
       "mar       -1.19   0.23       2.09\n",
       "paro      -0.42   0.67       0.57\n",
       "prio       3.16 <0.005       9.33\n",
       "---\n",
       "Concordance = 0.61\n",
       "Partial AIC = 1173.77\n",
       "log-likelihood ratio test = 23.77 on 6 df\n",
       "-log2(p) of ll-ratio test = 10.77"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cph.fit(rossi, 'week', 'arrest', strata=['wexp'])\n",
    "cph.print_summary(model=\"wexp in strata\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The ``p_value_threshold`` is set at 0.01. Even under the null hypothesis of no violations, some\n",
      "covariates will be below the threshold by chance. This is compounded when there are many covariates.\n",
      "Similarly, when there are lots of observations, even minor deviances from the proportional hazard\n",
      "assumption will be flagged.\n",
      "\n",
      "With that in mind, it's best to use a combination of statistical tests and visual tests to determine\n",
      "the most serious violations. Produce visual plots using ``check_assumptions(..., show_plots=True)``\n",
      "and looking for non-constant lines. See link [A] below for a full example.\n",
      "\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>null_distribution</th>\n",
       "      <td>chi squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degrees_of_freedom</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>&lt;lifelines.CoxPHFitter: fitted with 432 total ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_name</th>\n",
       "      <td>proportional_hazard_test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>test_statistic</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">age</th>\n",
       "      <th>km</th>\n",
       "      <td>11.29</td>\n",
       "      <td>&lt;0.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>4.62</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">fin</th>\n",
       "      <th>km</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">mar</th>\n",
       "      <th>km</th>\n",
       "      <td>0.53</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>1.31</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">paro</th>\n",
       "      <th>km</th>\n",
       "      <td>0.09</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">prio</th>\n",
       "      <th>km</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">race</th>\n",
       "      <th>km</th>\n",
       "      <td>1.47</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>0.64</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
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       "<IPython.core.display.HTML object>"
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     "text": [
      "\n",
      "\n",
      "1. Variable 'age' failed the non-proportional test: p-value is 0.0008.\n",
      "\n",
      "   Advice 1: the functional form of the variable 'age' might be incorrect. That is, there may be\n",
      "non-linear terms missing. The proportional hazard test used is very sensitive to incorrect\n",
      "functional forms. See documentation in link [D] below on how to specify a functional form.\n",
      "\n",
      "   Advice 2: try binning the variable 'age' using pd.cut, and then specify it in `strata=['age',\n",
      "...]` in the call in `.fit`. See documentation in link [B] below.\n",
      "\n",
      "   Advice 3: try adding an interaction term with your time variable. See documentation in link [C]\n",
      "below.\n",
      "\n",
      "\n",
      "---\n",
      "[A]  https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html\n",
      "[B]  https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html#Bin-variable-and-stratify-on-it\n",
      "[C]  https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html#Introduce-time-varying-covariates\n",
      "[D]  https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html#Modify-the-functional-form\n",
      "[E]  https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html#Stratification\n",
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 284,
       "width": 424
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cph.check_assumptions(rossi, show_plots=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since `age` is still violating the proportional hazard assumption, we need to model it better.  From the residual plots above, we can see a the effect of age start to become negative over time. This will be relevant later. Below, we present three options to handle `age`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Modify the functional form\n",
    "\n",
    "The proportional hazard test is very sensitive (i.e. lots of false positives) when the functional form of a variable is incorrect. For example, if the association between a covariate and the log-hazard is non-linear, but the model has only a linear term included, then the proportional hazard test can raise a false positive. \n",
    "\n",
    "The modeller can choose to add quadratic or cubic terms, i.e:\n",
    "```\n",
    "rossi['age**2'] = (rossi['age'] - rossi['age'].mean())**2\n",
    "rossi['age**3'] = (rossi['age'] - rossi['age'].mean())**3\n",
    "```\n",
    "\n",
    "but I think a more correct way to include non-linear terms is to use basis splines:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>lifelines.CoxPHFitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration col</th>\n",
       "      <td>'week'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event col</th>\n",
       "      <td>'arrest'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>strata</th>\n",
       "      <td>[wexp]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baseline estimation</th>\n",
       "      <td>breslow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of observations</th>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of events observed</th>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partial log-likelihood</th>\n",
       "      <td>-579.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2020-07-26 22:19:25 UTC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>spline_model</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef</th>\n",
       "      <th>exp(coef)</th>\n",
       "      <th>se(coef)</th>\n",
       "      <th>coef lower 95%</th>\n",
       "      <th>coef upper 95%</th>\n",
       "      <th>exp(coef) lower 95%</th>\n",
       "      <th>exp(coef) upper 95%</th>\n",
       "      <th>z</th>\n",
       "      <th>p</th>\n",
       "      <th>-log2(p)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>covariate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bs(age, df=4, lower_bound=10, upper_bound=50)[0]</th>\n",
       "      <td>-2.95</td>\n",
       "      <td>0.05</td>\n",
       "      <td>8.32</td>\n",
       "      <td>-19.26</td>\n",
       "      <td>13.37</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.39e+05</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bs(age, df=4, lower_bound=10, upper_bound=50)[1]</th>\n",
       "      <td>-5.48</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.23</td>\n",
       "      <td>-17.69</td>\n",
       "      <td>6.73</td>\n",
       "      <td>0.00</td>\n",
       "      <td>839.48</td>\n",
       "      <td>-0.88</td>\n",
       "      <td>0.38</td>\n",
       "      <td>1.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bs(age, df=4, lower_bound=10, upper_bound=50)[2]</th>\n",
       "      <td>-3.69</td>\n",
       "      <td>0.03</td>\n",
       "      <td>8.88</td>\n",
       "      <td>-21.10</td>\n",
       "      <td>13.72</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.09e+05</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bs(age, df=4, lower_bound=10, upper_bound=50)[3]</th>\n",
       "      <td>-6.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.75</td>\n",
       "      <td>-19.26</td>\n",
       "      <td>7.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1351.35</td>\n",
       "      <td>-0.89</td>\n",
       "      <td>0.37</td>\n",
       "      <td>1.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fin</th>\n",
       "      <td>-0.37</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.19</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1.01</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>0.05</td>\n",
       "      <td>4.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race</th>\n",
       "      <td>0.35</td>\n",
       "      <td>1.42</td>\n",
       "      <td>0.31</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.77</td>\n",
       "      <td>2.60</td>\n",
       "      <td>1.13</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mar</th>\n",
       "      <td>-0.39</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.38</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.32</td>\n",
       "      <td>1.43</td>\n",
       "      <td>-1.02</td>\n",
       "      <td>0.31</td>\n",
       "      <td>1.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paro</th>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.20</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.33</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prio</th>\n",
       "      <td>0.09</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.15</td>\n",
       "      <td>1.04</td>\n",
       "      <td>1.16</td>\n",
       "      <td>3.22</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>9.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Concordance</th>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partial AIC</th>\n",
       "      <td>1176.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood ratio test</th>\n",
       "      <td>26.82 on 9 df</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-log2(p) of ll-ratio test</th>\n",
       "      <td>9.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "\\begin{tabular}{lrrrrrrrrrr}\n",
       "\\toprule\n",
       "{} &  coef &  exp(coef) &  se(coef) &  coef lower 95\\% &  coef upper 95\\% &  exp(coef) lower 95\\% &  exp(coef) upper 95\\% &     z &    p &  -log2(p) \\\\\n",
       "covariate                                        &       &            &           &                 &                 &                      &                      &       &      &           \\\\\n",
       "\\midrule\n",
       "bs(age, df=4, lower\\_bound=10, upper\\_bound=50)[0] & -2.95 &       0.05 &      8.32 &          -19.26 &           13.37 &                 0.00 &            639309.54 & -0.35 & 0.72 &      0.47 \\\\\n",
       "bs(age, df=4, lower\\_bound=10, upper\\_bound=50)[1] & -5.48 &       0.00 &      6.23 &          -17.69 &            6.73 &                 0.00 &               839.48 & -0.88 & 0.38 &      1.40 \\\\\n",
       "bs(age, df=4, lower\\_bound=10, upper\\_bound=50)[2] & -3.69 &       0.03 &      8.88 &          -21.10 &           13.72 &                 0.00 &            909335.05 & -0.42 & 0.68 &      0.56 \\\\\n",
       "bs(age, df=4, lower\\_bound=10, upper\\_bound=50)[3] & -6.02 &       0.00 &      6.75 &          -19.26 &            7.21 &                 0.00 &              1351.35 & -0.89 & 0.37 &      1.43 \\\\\n",
       "fin                                              & -0.37 &       0.69 &      0.19 &           -0.75 &            0.01 &                 0.47 &                 1.01 & -1.93 & 0.05 &      4.22 \\\\\n",
       "race                                             &  0.35 &       1.42 &      0.31 &           -0.26 &            0.95 &                 0.77 &                 2.60 &  1.13 & 0.26 &      1.95 \\\\\n",
       "mar                                              & -0.39 &       0.67 &      0.38 &           -1.15 &            0.36 &                 0.32 &                 1.43 & -1.02 & 0.31 &      1.71 \\\\\n",
       "paro                                             & -0.10 &       0.90 &      0.20 &           -0.49 &            0.28 &                 0.61 &                 1.33 & -0.53 & 0.60 &      0.75 \\\\\n",
       "prio                                             &  0.09 &       1.10 &      0.03 &            0.04 &            0.15 &                 1.04 &                 1.16 &  3.22 & 0.00 &      9.59 \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.CoxPHFitter: fitted with 432 total observations, 318 right-censored observations>\n",
       "             duration col = 'week'\n",
       "                event col = 'arrest'\n",
       "                   strata = ['wexp']\n",
       "      baseline estimation = breslow\n",
       "   number of observations = 432\n",
       "number of events observed = 114\n",
       "   partial log-likelihood = -579.36\n",
       "         time fit was run = 2020-07-26 22:19:25 UTC\n",
       "                    model = spline_model\n",
       "\n",
       "---\n",
       "                                                   coef  exp(coef)   se(coef)   coef lower 95%   coef upper 95%  exp(coef) lower 95%  exp(coef) upper 95%\n",
       "covariate                                                                                                                                                \n",
       "bs(age, df=4, lower_bound=10, upper_bound=50)[0]  -2.95       0.05       8.32           -19.26            13.37                 0.00             6.39e+05\n",
       "bs(age, df=4, lower_bound=10, upper_bound=50)[1]  -5.48       0.00       6.23           -17.69             6.73                 0.00               839.48\n",
       "bs(age, df=4, lower_bound=10, upper_bound=50)[2]  -3.69       0.03       8.88           -21.10            13.72                 0.00             9.09e+05\n",
       "bs(age, df=4, lower_bound=10, upper_bound=50)[3]  -6.02       0.00       6.75           -19.26             7.21                 0.00              1351.35\n",
       "fin                                               -0.37       0.69       0.19            -0.75             0.01                 0.47                 1.01\n",
       "race                                               0.35       1.42       0.31            -0.26             0.95                 0.77                 2.60\n",
       "mar                                               -0.39       0.67       0.38            -1.15             0.36                 0.32                 1.43\n",
       "paro                                              -0.10       0.90       0.20            -0.49             0.28                 0.61                 1.33\n",
       "prio                                               0.09       1.10       0.03             0.04             0.15                 1.04                 1.16\n",
       "                                                     z      p   -log2(p)\n",
       "covariate                                                               \n",
       "bs(age, df=4, lower_bound=10, upper_bound=50)[0] -0.35   0.72       0.47\n",
       "bs(age, df=4, lower_bound=10, upper_bound=50)[1] -0.88   0.38       1.40\n",
       "bs(age, df=4, lower_bound=10, upper_bound=50)[2] -0.42   0.68       0.56\n",
       "bs(age, df=4, lower_bound=10, upper_bound=50)[3] -0.89   0.37       1.43\n",
       "fin                                              -1.93   0.05       4.22\n",
       "race                                              1.13   0.26       1.95\n",
       "mar                                              -1.02   0.31       1.71\n",
       "paro                                             -0.53   0.60       0.75\n",
       "prio                                              3.22 <0.005       9.59\n",
       "---\n",
       "Concordance = 0.62\n",
       "Partial AIC = 1176.72\n",
       "log-likelihood ratio test = 26.82 on 9 df\n",
       "-log2(p) of ll-ratio test = 9.38"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Proportional hazard assumption looks okay.\n"
     ]
    }
   ],
   "source": [
    "cph.fit(rossi, 'week', 'arrest', strata=['wexp'], formula=\"bs(age, df=4, lower_bound=10, upper_bound=50) + fin +race + mar + paro + prio\")\n",
    "cph.print_summary(model=\"spline_model\"); print()\n",
    "cph.check_assumptions(rossi, show_plots=True, p_value_threshold=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see may still have potentially _some_ violation, but it's a heck of a lot less. Also, interestingly, when we include these non-linear terms for `age`, the `wexp` proportionality violation disappears. It is not uncommon to see changing the functional form of one variable effects other's proportional tests, usually positively. So, we could remove the `strata=['wexp']` if we wished. \n",
    "\n",
    "#### Bin variable and stratify on it\n",
    "\n",
    "\n",
    "The second option proposed is to bin the variable into equal-sized bins, and stratify like we did with `wexp`. There is a trade off here between estimation and information-loss. If we have large bins, we will lose information (since different values are now binned together), but we need to estimate less new baseline hazards. On the other hand, with tiny bins, we allow the `age` data to have the most \"wiggle room\", but must compute many baseline hazards each of which has a smaller sample size. Like most things, the optimial value is somewhere inbetween."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>age_strata</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27</td>\n",
       "      <td>(24, 27]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>(15, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>(18, 21]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23</td>\n",
       "      <td>(21, 24]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>(18, 21]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age age_strata\n",
       "0   27   (24, 27]\n",
       "1   18   (15, 18]\n",
       "2   19   (18, 21]\n",
       "3   23   (21, 24]\n",
       "4   19   (18, 21]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rossi_strata_age = rossi.copy()\n",
    "rossi_strata_age['age_strata'] = pd.cut(rossi_strata_age['age'], np.arange(0, 80, 3))\n",
    "\n",
    "rossi_strata_age[['age', 'age_strata']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lifelines.CoxPHFitter: fitted with 432 total observations, 318 right-censored observations>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop the orignal, redundant, age column\n",
    "rossi_strata_age = rossi_strata_age.drop('age', axis=1)\n",
    "cph.fit(rossi_strata_age, 'week', 'arrest', strata=['age_strata', 'wexp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>lifelines.CoxPHFitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration col</th>\n",
       "      <td>'week'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event col</th>\n",
       "      <td>'arrest'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>strata</th>\n",
       "      <td>[age_strata, wexp]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baseline estimation</th>\n",
       "      <td>breslow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of observations</th>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of events observed</th>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partial log-likelihood</th>\n",
       "      <td>-392.443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2020-07-26 22:19:46 UTC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>stratified age and wexp</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef</th>\n",
       "      <th>exp(coef)</th>\n",
       "      <th>se(coef)</th>\n",
       "      <th>coef lower 95%</th>\n",
       "      <th>coef upper 95%</th>\n",
       "      <th>exp(coef) lower 95%</th>\n",
       "      <th>exp(coef) upper 95%</th>\n",
       "      <th>z</th>\n",
       "      <th>p</th>\n",
       "      <th>-log2(p)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>covariate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fin</th>\n",
       "      <td>-0.395</td>\n",
       "      <td>0.674</td>\n",
       "      <td>0.197</td>\n",
       "      <td>-0.781</td>\n",
       "      <td>-0.009</td>\n",
       "      <td>0.458</td>\n",
       "      <td>0.991</td>\n",
       "      <td>-2.004</td>\n",
       "      <td>0.045</td>\n",
       "      <td>4.472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race</th>\n",
       "      <td>0.280</td>\n",
       "      <td>1.324</td>\n",
       "      <td>0.313</td>\n",
       "      <td>-0.334</td>\n",
       "      <td>0.895</td>\n",
       "      <td>0.716</td>\n",
       "      <td>2.447</td>\n",
       "      <td>0.895</td>\n",
       "      <td>0.371</td>\n",
       "      <td>1.431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mar</th>\n",
       "      <td>-0.194</td>\n",
       "      <td>0.824</td>\n",
       "      <td>0.392</td>\n",
       "      <td>-0.961</td>\n",
       "      <td>0.574</td>\n",
       "      <td>0.382</td>\n",
       "      <td>1.776</td>\n",
       "      <td>-0.494</td>\n",
       "      <td>0.621</td>\n",
       "      <td>0.687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paro</th>\n",
       "      <td>-0.163</td>\n",
       "      <td>0.849</td>\n",
       "      <td>0.200</td>\n",
       "      <td>-0.555</td>\n",
       "      <td>0.228</td>\n",
       "      <td>0.574</td>\n",
       "      <td>1.256</td>\n",
       "      <td>-0.818</td>\n",
       "      <td>0.413</td>\n",
       "      <td>1.275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prio</th>\n",
       "      <td>0.080</td>\n",
       "      <td>1.084</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.135</td>\n",
       "      <td>1.025</td>\n",
       "      <td>1.145</td>\n",
       "      <td>2.854</td>\n",
       "      <td>0.004</td>\n",
       "      <td>7.857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Concordance</th>\n",
       "      <td>0.582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partial AIC</th>\n",
       "      <td>794.887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood ratio test</th>\n",
       "      <td>13.247 on 5 df</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-log2(p) of ll-ratio test</th>\n",
       "      <td>5.562</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "\\begin{tabular}{lrrrrrrrrrr}\n",
       "\\toprule\n",
       "{} &   coef &  exp(coef) &  se(coef) &  coef lower 95\\% &  coef upper 95\\% &  exp(coef) lower 95\\% &  exp(coef) upper 95\\% &      z &     p &  -log2(p) \\\\\n",
       "covariate &        &            &           &                 &                 &                      &                      &        &       &           \\\\\n",
       "\\midrule\n",
       "fin       & -0.395 &      0.674 &     0.197 &          -0.781 &          -0.009 &                0.458 &                0.991 & -2.004 & 0.045 &     4.472 \\\\\n",
       "race      &  0.280 &      1.324 &     0.313 &          -0.334 &           0.895 &                0.716 &                2.447 &  0.895 & 0.371 &     1.431 \\\\\n",
       "mar       & -0.194 &      0.824 &     0.392 &          -0.961 &           0.574 &                0.382 &                1.776 & -0.494 & 0.621 &     0.687 \\\\\n",
       "paro      & -0.163 &      0.849 &     0.200 &          -0.555 &           0.228 &                0.574 &                1.256 & -0.818 & 0.413 &     1.275 \\\\\n",
       "prio      &  0.080 &      1.084 &     0.028 &           0.025 &           0.135 &                1.025 &                1.145 &  2.854 & 0.004 &     7.857 \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.CoxPHFitter: fitted with 432 total observations, 318 right-censored observations>\n",
       "             duration col = 'week'\n",
       "                event col = 'arrest'\n",
       "                   strata = ['age_strata', 'wexp']\n",
       "      baseline estimation = breslow\n",
       "   number of observations = 432\n",
       "number of events observed = 114\n",
       "   partial log-likelihood = -392.443\n",
       "         time fit was run = 2020-07-26 22:19:46 UTC\n",
       "                    model = stratified age and wexp\n",
       "\n",
       "---\n",
       "            coef  exp(coef)   se(coef)   coef lower 95%   coef upper 95%  exp(coef) lower 95%  exp(coef) upper 95%\n",
       "covariate                                                                                                         \n",
       "fin       -0.395      0.674      0.197           -0.781           -0.009                0.458                0.991\n",
       "race       0.280      1.324      0.313           -0.334            0.895                0.716                2.447\n",
       "mar       -0.194      0.824      0.392           -0.961            0.574                0.382                1.776\n",
       "paro      -0.163      0.849      0.200           -0.555            0.228                0.574                1.256\n",
       "prio       0.080      1.084      0.028            0.025            0.135                1.025                1.145\n",
       "               z     p   -log2(p)\n",
       "covariate                        \n",
       "fin       -2.004 0.045      4.472\n",
       "race       0.895 0.371      1.431\n",
       "mar       -0.494 0.621      0.687\n",
       "paro      -0.818 0.413      1.275\n",
       "prio       2.854 0.004      7.857\n",
       "---\n",
       "Concordance = 0.582\n",
       "Partial AIC = 794.887\n",
       "log-likelihood ratio test = 13.247 on 5 df\n",
       "-log2(p) of ll-ratio test = 5.562"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='log(HR) (95% CI)'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 378
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cph.print_summary(3, model=\"stratified age and wexp\")\n",
    "cph.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportional hazard assumption looks okay.\n"
     ]
    }
   ],
   "source": [
    "cph.check_assumptions(rossi_strata_age)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Introduce time-varying covariates\n",
    "\n",
    "Our second option to correct variables that violate the proportional hazard assumption is to model the time-varying component directly. This is done in two steps. The first is to transform your dataset into _episodic format_. This means that we split a subject from a single row into $n$ new rows, and each new row represents some time period for the subject. It's okay that the variables are static over this new time periods - we'll introduce some time-varying covariates later.\n",
    "\n",
    "See below for how to do this in _lifelines_:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stop</th>\n",
       "      <th>start</th>\n",
       "      <th>arrest</th>\n",
       "      <th>age</th>\n",
       "      <th>fin</th>\n",
       "      <th>id</th>\n",
       "      <th>mar</th>\n",
       "      <th>paro</th>\n",
       "      <th>prio</th>\n",
       "      <th>race</th>\n",
       "      <th>wexp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    stop  start  arrest  age  fin  id  mar  paro  prio  race  wexp\n",
       "0    1.0    0.0       0   27    0   0    0     1     3     1     0\n",
       "1    2.0    1.0       0   27    0   0    0     1     3     1     0\n",
       "2    3.0    2.0       0   27    0   0    0     1     3     1     0\n",
       "3    4.0    3.0       0   27    0   0    0     1     3     1     0\n",
       "4    5.0    4.0       0   27    0   0    0     1     3     1     0\n",
       "5    6.0    5.0       0   27    0   0    0     1     3     1     0\n",
       "6    7.0    6.0       0   27    0   0    0     1     3     1     0\n",
       "7    8.0    7.0       0   27    0   0    0     1     3     1     0\n",
       "8    9.0    8.0       0   27    0   0    0     1     3     1     0\n",
       "9   10.0    9.0       0   27    0   0    0     1     3     1     0\n",
       "10  11.0   10.0       0   27    0   0    0     1     3     1     0\n",
       "11  12.0   11.0       0   27    0   0    0     1     3     1     0\n",
       "12  13.0   12.0       0   27    0   0    0     1     3     1     0\n",
       "13  14.0   13.0       0   27    0   0    0     1     3     1     0\n",
       "14  15.0   14.0       0   27    0   0    0     1     3     1     0\n",
       "15  16.0   15.0       0   27    0   0    0     1     3     1     0\n",
       "16  17.0   16.0       0   27    0   0    0     1     3     1     0\n",
       "17  18.0   17.0       0   27    0   0    0     1     3     1     0\n",
       "18  19.0   18.0       0   27    0   0    0     1     3     1     0\n",
       "19  20.0   19.0       1   27    0   0    0     1     3     1     0\n",
       "20   1.0    0.0       0   18    0   1    0     1     8     1     0\n",
       "21   2.0    1.0       0   18    0   1    0     1     8     1     0\n",
       "22   3.0    2.0       0   18    0   1    0     1     8     1     0\n",
       "23   4.0    3.0       0   18    0   1    0     1     8     1     0\n",
       "24   5.0    4.0       0   18    0   1    0     1     8     1     0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lifelines.utils import to_episodic_format\n",
    "\n",
    "# the time_gaps parameter specifies how large or small you want the periods to be. \n",
    "rossi_long = to_episodic_format(rossi, duration_col='week', event_col='arrest', time_gaps=1.)\n",
    "rossi_long.head(25)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each subject is given a new id (but can be specified as well if already provided in the dataframe). This id is used to track subjects over time. Notice the `arrest` col is 0 for all periods prior to their (possible) event as well. \n",
    "\n",
    "Above I mentioned there were two steps to correct `age`. The first was to convert to a episodic format. The second is to create an interaction term between `age` and `stop`. This is a time-varying variable.\n",
    "\n",
    "Instead of `CoxPHFitter`, we must use `CoxTimeVaryingFitter` instead since we are working with a episodic dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "rossi_long['time*age'] = rossi_long['age'] * rossi_long['stop']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lifelines.CoxTimeVaryingFitter: fitted with 19809 periods, 432 subjects, 114 events>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lifelines import CoxTimeVaryingFitter\n",
    "ctv = CoxTimeVaryingFitter()\n",
    "\n",
    "ctv.fit(rossi_long, \n",
    "        id_col='id', \n",
    "        event_col='arrest', \n",
    "        start_col='start', \n",
    "        stop_col='stop', \n",
    "        strata=['wexp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>lifelines.CoxTimeVaryingFitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event col</th>\n",
       "      <td>'arrest'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>strata</th>\n",
       "      <td>[wexp]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of subjects</th>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of periods</th>\n",
       "      <td>19809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of events</th>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partial log-likelihood</th>\n",
       "      <td>-575.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2020-07-26 22:19:49 UTC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>age * time interaction</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef</th>\n",
       "      <th>exp(coef)</th>\n",
       "      <th>se(coef)</th>\n",
       "      <th>coef lower 95%</th>\n",
       "      <th>coef upper 95%</th>\n",
       "      <th>exp(coef) lower 95%</th>\n",
       "      <th>exp(coef) upper 95%</th>\n",
       "      <th>z</th>\n",
       "      <th>p</th>\n",
       "      <th>-log2(p)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>covariate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.073</td>\n",
       "      <td>1.075</td>\n",
       "      <td>0.040</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.995</td>\n",
       "      <td>1.163</td>\n",
       "      <td>1.830</td>\n",
       "      <td>0.067</td>\n",
       "      <td>3.893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fin</th>\n",
       "      <td>-0.386</td>\n",
       "      <td>0.680</td>\n",
       "      <td>0.191</td>\n",
       "      <td>-0.760</td>\n",
       "      <td>-0.011</td>\n",
       "      <td>0.468</td>\n",
       "      <td>0.989</td>\n",
       "      <td>-2.018</td>\n",
       "      <td>0.044</td>\n",
       "      <td>4.520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mar</th>\n",
       "      <td>-0.397</td>\n",
       "      <td>0.672</td>\n",
       "      <td>0.382</td>\n",
       "      <td>-1.147</td>\n",
       "      <td>0.352</td>\n",
       "      <td>0.318</td>\n",
       "      <td>1.422</td>\n",
       "      <td>-1.039</td>\n",
       "      <td>0.299</td>\n",
       "      <td>1.743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paro</th>\n",
       "      <td>-0.098</td>\n",
       "      <td>0.907</td>\n",
       "      <td>0.196</td>\n",
       "      <td>-0.481</td>\n",
       "      <td>0.285</td>\n",
       "      <td>0.618</td>\n",
       "      <td>1.330</td>\n",
       "      <td>-0.501</td>\n",
       "      <td>0.616</td>\n",
       "      <td>0.698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prio</th>\n",
       "      <td>0.090</td>\n",
       "      <td>1.094</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.146</td>\n",
       "      <td>1.035</td>\n",
       "      <td>1.158</td>\n",
       "      <td>3.152</td>\n",
       "      <td>0.002</td>\n",
       "      <td>9.267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race</th>\n",
       "      <td>0.295</td>\n",
       "      <td>1.343</td>\n",
       "      <td>0.308</td>\n",
       "      <td>-0.310</td>\n",
       "      <td>0.899</td>\n",
       "      <td>0.733</td>\n",
       "      <td>2.458</td>\n",
       "      <td>0.955</td>\n",
       "      <td>0.340</td>\n",
       "      <td>1.558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time*age</th>\n",
       "      <td>-0.005</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.002</td>\n",
       "      <td>-0.008</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>0.992</td>\n",
       "      <td>0.998</td>\n",
       "      <td>-3.337</td>\n",
       "      <td>0.001</td>\n",
       "      <td>10.203</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Partial AIC</th>\n",
       "      <td>1164.160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood ratio test</th>\n",
       "      <td>35.386 on 7 df</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-log2(p) of ll-ratio test</th>\n",
       "      <td>16.689</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "\\begin{tabular}{lrrrrrrrrrr}\n",
       "\\toprule\n",
       "{} &   coef &  exp(coef) &  se(coef) &  coef lower 95\\% &  coef upper 95\\% &  exp(coef) lower 95\\% &  exp(coef) upper 95\\% &      z &     p &  -log2(p) \\\\\n",
       "covariate &        &            &           &                 &                 &                      &                      &        &       &           \\\\\n",
       "\\midrule\n",
       "age       &  0.073 &      1.075 &     0.040 &          -0.005 &           0.151 &                0.995 &                1.163 &  1.830 & 0.067 &     3.893 \\\\\n",
       "fin       & -0.386 &      0.680 &     0.191 &          -0.760 &          -0.011 &                0.468 &                0.989 & -2.018 & 0.044 &     4.520 \\\\\n",
       "mar       & -0.397 &      0.672 &     0.382 &          -1.147 &           0.352 &                0.318 &                1.422 & -1.039 & 0.299 &     1.743 \\\\\n",
       "paro      & -0.098 &      0.907 &     0.196 &          -0.481 &           0.285 &                0.618 &                1.330 & -0.501 & 0.616 &     0.698 \\\\\n",
       "prio      &  0.090 &      1.094 &     0.029 &           0.034 &           0.146 &                1.035 &                1.158 &  3.152 & 0.002 &     9.267 \\\\\n",
       "race      &  0.295 &      1.343 &     0.308 &          -0.310 &           0.899 &                0.733 &                2.458 &  0.955 & 0.340 &     1.558 \\\\\n",
       "time*age  & -0.005 &      0.995 &     0.002 &          -0.008 &          -0.002 &                0.992 &                0.998 & -3.337 & 0.001 &    10.203 \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.CoxTimeVaryingFitter: fitted with 19809 periods, 432 subjects, 114 events>\n",
       "         event col = 'arrest'\n",
       "            strata = ['wexp']\n",
       "number of subjects = 432\n",
       " number of periods = 19809\n",
       "  number of events = 114\n",
       "partial log-likelihood = -575.080\n",
       "  time fit was run = 2020-07-26 22:19:49 UTC\n",
       "             model = age * time interaction\n",
       "\n",
       "---\n",
       "            coef  exp(coef)   se(coef)   coef lower 95%   coef upper 95%  exp(coef) lower 95%  exp(coef) upper 95%\n",
       "covariate                                                                                                         \n",
       "age        0.073      1.075      0.040           -0.005            0.151                0.995                1.163\n",
       "fin       -0.386      0.680      0.191           -0.760           -0.011                0.468                0.989\n",
       "mar       -0.397      0.672      0.382           -1.147            0.352                0.318                1.422\n",
       "paro      -0.098      0.907      0.196           -0.481            0.285                0.618                1.330\n",
       "prio       0.090      1.094      0.029            0.034            0.146                1.035                1.158\n",
       "race       0.295      1.343      0.308           -0.310            0.899                0.733                2.458\n",
       "time*age  -0.005      0.995      0.002           -0.008           -0.002                0.992                0.998\n",
       "               z     p   -log2(p)\n",
       "covariate                        \n",
       "age        1.830 0.067      3.893\n",
       "fin       -2.018 0.044      4.520\n",
       "mar       -1.039 0.299      1.743\n",
       "paro      -0.501 0.616      0.698\n",
       "prio       3.152 0.002      9.267\n",
       "race       0.955 0.340      1.558\n",
       "time*age  -3.337 0.001     10.203\n",
       "---\n",
       "Partial AIC = 1164.160\n",
       "log-likelihood ratio test = 35.386 on 7 df\n",
       "-log2(p) of ll-ratio test = 16.689"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ctv.print_summary(3, model=\"age * time interaction\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='log(HR) (95% CI)'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctv.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above scaled Schoenfeld residual plots for `age`, we can see there is a slight negative effect for higher time values. This is confirmed in the output of the `CoxTimeVaryingFitter`: we see that the coefficient for `time*age` is -0.005.\n",
    "\n",
    "#### Conclusion\n",
    "\n",
    "The point estimates and the standard errors are very close to each other using either option, we can feel confident that either approach is okay to proceed. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Do I need to care about the proportional hazard assumption?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may be surprised that often you don't need to care about the proportional hazard assumption. There are many reasons why not:\n",
    "\n",
    "1. If your goal is survival prediction, then you don't need to care about proportional hazards. Your goal is to maximize some score, irrelevant of how predictions are generated. \n",
    "\n",
    "2. Given a large enough sample size, even very small violations of proportional hazards will show up.  \n",
    "\n",
    "3. There are legitimate reasons to assume that all datasets will violate the proportional hazards assumption. This is detailed well in Stensrud & Hernán's \"Why Test for Proportional Hazards?\" [1]. \n",
    "\n",
    "4. “Even if the hazards were not proportional, altering the model to fit a set of assumptions fundamentally changes the scientific question. As Tukey said,”Better an approximate answer to the exact question, rather than an exact answer to the approximate question.” If you were to fit the Cox model in the presence of non-proportional hazards, what is the net effect? Slightly less power. In fact, you can recover most of that power with robust standard errors (specify robust=True). In this case the interpretation of the (exponentiated) model coefficient is a time-weighted average of the hazard ratio–I do this every single time.” from AdamO, slightly modified to fit lifelines [2]\n",
    "\n",
    "Given the above considerations, the status quo is still to check for proportional hazards. So if you are avoiding testing for proportional hazards, be sure to understand and able to answer _why_ you are avoiding testing. \n",
    "\n",
    "\n",
    "1.  Stensrud MJ, Hernán MA. Why Test for Proportional Hazards? JAMA. Published online March 13, 2020. doi:10.1001/jama.2020.1267\n",
    "2. AdamO (https://stats.stackexchange.com/users/8013/adamo), Checking the proportional hazard assumption, URL (version: 2019-04-05): https://stats.stackexchange.com/q/400981"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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